{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Forecasting with sktime - appendix: forecasting, supervised regression, and pitfalls in confusing the two\n",
    "\n",
    "This notebook provides some supplementary explanation about the relation between forecasting as implemented in `sktime`, and the very common supervised prediction tasks as supported by `scikit-learn` and similar toolboxes.\n",
    "\n",
    "Key points discussed in this notebook:\n",
    "\n",
    "* Forecasting is not the same as supervised prediction;\n",
    "* Even though forecasting can be \"solved\" by algorithms for supervised prediction, this is indirect and requires careful composition;\n",
    "* From an interface perspective, this is correctly formulated as \"reduction\", i.e., use of a supervised predictor as a component within a forecaster;\n",
    "* There are a number of pitfalls if this is manually done - such as, over-optimistic performance evaluation, information leakage, or \"predicting the past\" type errors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# general imports\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The pitfalls of misdiagnosing forecasting as supervised regression\n",
    "\n",
    "A common mistake is to misidentify a forecasting problem as supervised regression - after all, in both we predict numbers, so surely this must be the same thing?\n",
    "\n",
    "Indeed we predict numbers in both, but the set-up is different:\n",
    "\n",
    "* In supervised regression, we predict *label/target variables* from *feature variables*, in a cross-sectional set-up. This is after training on label/feature examples.\n",
    "* In forecasting, we predict *future values* from *past values*, of *the same variable*, in a temporal/sequential set-up. This is after training on the past.\n",
    "\n",
    "In the common data frame representation:\n",
    "\n",
    "* In supervised regression, we predict entries in a column from other columns. For this, we mainly make use of the statistical relation between those columns, learnt from examples of complete rows. The rows are all assumed exchangeable.\n",
    "* In forecasting, we predict new rows, assuming temporal ordering in the rows. For this, we mainly make use of the statistical relation between previous and subsequent rows, learnt from the example of the observed sequence of rows. The rows are not exchangeable, but in temporal sequence."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pitfall 1: over-optimism in performance evaluation, false confidence in \"broken\" forecasters\n",
    "\n",
    "Confusing the two tasks may lead to information leakage, and over-optimistic performance evaluation. This is because in supervised regression the ordering of rows does not matter, and train/test split is usually performed uniformly. In forecasting, the ordering does matter, both in training and in evaluation.\n",
    "\n",
    "As subtle as it seems, this may have major practical consequences - since it can lead to the mistaken belief that a \"broken\" method is performant, which can cause damage to health, property, and other assets in real-life deployment.\n",
    "\n",
    "The example below shows \"problematic\" performance estimation, when mistakenly using the regression evaluation workflow for forecasting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-10T16:07:06.641893Z",
     "iopub.status.busy": "2021-04-10T16:07:06.618023Z",
     "iopub.status.idle": "2021-04-10T16:07:06.787235Z",
     "shell.execute_reply": "2021-04-10T16:07:06.787715Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sktime.datasets import load_airline\n",
    "from sktime.split import temporal_train_test_split\n",
    "from sktime.utils.plotting import plot_series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = load_airline()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_train, y_test = train_test_split(y)\n",
    "plot_series(y_train.sort_index(), y_test.sort_index(), labels=[\"y_train\", \"y_test\"]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This leads to leakage:\n",
    "\n",
    "> The data you are using to train a machine learning algorithm happens to have the information you are trying to predict.\n",
    "\n",
    "But `train_test_split(y, shuffle=False)` works, which is what `temporal_train_test_split(y)` does in `sktime`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-10T16:07:06.861616Z",
     "iopub.status.busy": "2021-04-10T16:07:06.861025Z",
     "iopub.status.idle": "2021-04-10T16:07:07.016695Z",
     "shell.execute_reply": "2021-04-10T16:07:07.017191Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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//vu9ft9FRERERERERNxq2zEvEsAaPRwMM05bOfaKHH+EJq2wBDqA7sZZV8nBZ2Lbe3aDP/Pfv8GrswcvLsKwhrf6vCuuuILbb7+d//znP1xxxRUA5OXlsXr1atasWdPieQsXLmTnzp28//77fPTRRwBER0d7rhkaGsp7771HdHQ0f/3rXzn//PPJyMigT58+XHvttUycOJFnn30Ws9nMtm3bsFqtnHXWWTzxxBMsXbqUPXv2ABAR0fbfWIiIiIiIiIiINOaujLR6sUkbwDAHYYkeSl1xJraiDCwR/fwRnpyCKiN7oNDQUK655hpeeuklz7FXX32VQYMGMXPmzFOeFxERgcViISkpiaSkJEJDQ/n888/58ssveeutt5gyZQojRozgD3/4AzExMbz99tsAHDp0iAsuuIBRo0YxYsQIrrjiCsaPH09QUBDR0dEYhuG5ppKRIiIiIiIiItIRtcd3ARAU1/blNW5aYhNYqoz0kmEJY/DiIq/OyX59OrbjuzhRGQlgYI0bQ7+rPvPq3m31gx/8gNNPP52jR4/Sv39/li9fzk033YRhGG0PvN727dspLy8nLi6uwfGqqir27dsHwF133cUtt9zC3//+dy644AKuuOIKhg8f7vW9RERERERERERa097KSHAtsalCychAUTLSS4ZhtKlV+mSx05aSt2ohYOBKSLr+jJ22FJOX12qriRMnMn78eF555RVmzZrFN998w+rVq9t1rfLycpKTk1m7dm2Tx9xbsu+//36uueYaVq9ezXvvvcdvfvMbXn/9debNm9eBVyEiIiIiIiIi0pC9usgz7zHIi03abqqMDCwlIztBeMo8Eua8QfGmh7AV7cEaO5KYqUsIT5nr1/vecsstPPHEExw9epQLLriAgQMHtnpOUFAQdru9wbFJkyaRm5uLxWJhyJAhLZ6bmppKamoqd955J1dffTUvvfQS8+bNa/aaIiIiIiIiIiLtYSv8FgBz5EBMwVFen38iGbnXp3FJ22hmZCcJT5lH/+s2M+S2Mvpft9nviUiAa665hiNHjvD888/z/e9/v03nDBkyhKysLLZt20ZBQQE1NTVccMEFTJs2jblz57JmzRoOHDjAhg0buO+++9i8eTNVVVXceuutrF27loMHD7J+/Xq++uorTjvtNM81y8vL+fjjjykoKKCystKfL1tEREREREREejDPvMh2tGgDWPu4kpF1pVk462p8Fpe0jZKRPVh0dDQLFiwgIiKCuXPntumcBQsW8N3vfpdzzz2X+Ph4/vnPf2IYBu+++y7f+c53uPnmm0lNTeWqq67i4MGDJCYmYjabOX78ODfccAOpqalceeWVXHzxxfz2t78F4KyzzuLHP/4xCxcuJD4+nkcffdSPr1pERERERESka6rIXMmRVydz4OlIjrw6mYrMlYEOqVvqyLxIAHNYEkZQJDgd2Er2+TI0aQPD6XQ6W39az1ZaWkp0dDQlJSVERTUs762uriYrK4uhQ4cSEhISoAjb7/zzz2fMmDE89dRTgQ6l3br710BERERERESkInNls/skEua8QXiK9i14I3fFbKoOfUjfC/5K5Nib23WNo69NpTZvCwlz3uyU7tXe4FT5tZOpMrKHKioqYuXKlaxdu5bFixcHOhwRERERERGRXq1o04OcSESCOyFZvOmhwAXVTdUWutq021sZCVpiE0haYNNDTZw4kaKiIh555BFGjhzpOT5mzBgOHjzY7Dl//etfufbaazsrRBEREREREZFew1a4hxOJSDcntqI9gQin23LUlGAvPwqAtR2btN2UjAwcJSN7qAMHDjR7/N1338VmszX7WGJioh8jEhEREREREel9nE4HJV8/Do7aZh41sMaObOa4tKTWvUk7vB/mkJh2X0cbtQNHycheZvDgwYEOQURERERERKRXsFfmk//B96k6+EEzj7patmOmLunssLo1z/KauNEduo4qIwNHycg20p6fwNF7LyIiIiIiIt1BReZKijY9SF1RBuaIfjiqi3DUFGOYQ4g79wmM4FgKPvwBztpSzOHJxJ37hJaneKn2uGteZFAH5kUCWGNHAOCoPo696jjm0LgOxyZto2RkK8xmMwC1tbWEhoYGOJreqbbWVcru/lqIiIiIiIiIdDWNt2XXlWQBYI7oT9Lc/xLUdywAtXlfU/LVo4QNv1SJyHawHXdXRnYsGWmyhmOOGIC9/Ai2ogzModN8EZ60QYeTkXa7nfT0dAYPHkxsbKwvYupSLBYLYWFh5OfnY7VaMZm0gLwzORwO8vPzCQsLw2JR7lxERERERES6pqbbsl1MwbGeRCSc3B6sxTXtUVvfph3UwTZtcH0t3MnIkH5KRnYWr7M7P/vZz0hLS2PRokXY7XZmzJjBhg0bCAsLY9WqVcycOdMPYQaOYRgkJyeTlZXV4hZq8S+TycSgQYMwDCPQoYiIiIiIiIg0q64og6bbsqGuuOGCFGusawO0ZhV6z1Fbhr3sENCxTdpu1thUqg//T1+LTuZ1MvLtt9/muuuuA+C///0vWVlZfPvtt/z973/nvvvuY/369T4PMtCCgoIYMWKEp11YOldQUJAqUkVERERERKRLs8SmYitIb3S06bZsd2WkvfwojtoyTEGRnRRh92dzb9IOS8Ic0qfD19MSm8DwOhlZUFBAUlISAO+++y5XXHEFqampfP/73+fJJ5/0eYBdhclkIiQkJNBhiIiIiIiIiEgXFDt1Sf3MSLfmt2WbQ2IwhyVirzyGrSiD4MTJnRpnd1Z73DebtN3cS2yUjOxcXpebJSYmsmvXLux2O++//z4XXnghAJWVlVowIiIiIiIiIiK9UnjKPCyRg1yfmKwE9U0jYc6bzS6pcVdLam6kd2yF9Zu0O7i8xs1TGVmyD6fD7pNrSuu8roy8+eabufLKK0lOTsYwDC644AIAvvjiC0aN6ni/voiIiIiIiIhId+Osq6GuIhuAgTftxhI1qMXnWmNTqT66DluhkpFtVZG5ktIdz7v+e+9KQgZ8h/CUeR26piVyEIY5GKe9hrrSA1hjhvsiVGmF18nI+++/n7S0NA4dOsQVV1xBcHAwAGazmV/96lc+D1BEREREREREpKurLdwFjjpMwbGYIwee8rnWPq7KyFpVRrZJRebKBi3w9ooc8lYtJGHOGx1KSBomM5aYFGzHv8FWtFfJyE7iVZu2zWbj/PPPZ9y4cdx5550MGDDA89iNN97IZZdd5vMARURERERERES6utr87QAEJUzAMIxTPleLU7xTtOlBXDM43ZyAQfGmhzp8bX0tOp9XyUir1cqOHTv8FYuIiIiIiIiISLdUm7cNgKD48a0+110ZWVe0V7MK26CuKANXAvJkTp/M3NQSm87n9QKb6667jhdeeMEfsYiIiIiIiIiIdEs19ZWRwW1IRloiB5+YVVh2yN+hdXuW+urFhgzPIqCOUGVk5/N6ZmRdXR0vvvgiH330EZMnTyY8PLzB448//rjPghMRERERERER6eqcTseJNu02JCMbzCos3IM1eqi/Q+zWYqcuaTAz0tWy7SRm6pIOX/vEZnMlIzuL18nInTt3MmnSJAAyMhp+oVqbiSAiIiIiIiIi0tPUlezHaSvHMAdj7TOqTedY+4ysX5yyB4Z+188Rdm9hwy/DsITjrKsAUxBBfUYRM3UJ4SlzO3xtd2WkvSIbR205pqCIDl9TTs3rZOQnn3zijzhERERERERERLold1Wkte9YDFPbUi1BsSOpRBV5bWE7vgtnXQWGNZzBP8lv83vcFuaQWEyh8Tiq8rEV7yU4YaLPri3N83pmpFtmZiYffPABVVVVADidjQeJioiIiIiIiIj0fDVeLK9x88wqLOz4EpaervroegCCk6f6NBHp5llio69Fp/A6GXn8+HHOP/98UlNTueSSS8jJyQFg0aJF3H333T4PUERERERERESkK6vN3wFAcPyENp/jbudWZWTrqrNdyciQfmf55fpaYtO5vE5G3nnnnVitVg4dOkRYWJjn+MKFC3n//fd9GpyIiIiIiIiISFdXm78N8LIyMsZVjWevzMVeXeyHqHqO6uwNAIT0O9sv11cysnN5Xdu6Zs0aPvjgAwYMGNDg+IgRIzh48KDPAhMRERERERER6ersFcewV+QABkF909p8nik4CnN4P+wV2diKMjAnn+G/ILuxutJD2MsOgWEmOPlMv9xDycjO5XVlZEVFRYOKSLfCwkKCg4N9EpSIiIiIiIiISHdQ415eE5vi9SZma5+RAK6N2tIsd4t2UMJETNZwv9zDk4ws3qudKJ3A62TkOeecwyuvvOL53DAMHA4Hjz76KOeee65PgxMRERERERER6crcm7SDvJgX6XaiIk/JyJZ4WrT7T/fbPazRw8Aw47RVYK/I9tt9xMXrNu1HH32U888/n82bN1NbW8s999zDN998Q2FhIevXr/dHjCIiIiIiIiIiXdKJZGTb50W6WWPrKyO1xblF1Uc/B/y3vAbAMAdhiR5KXXEmtqIMLBH9/XYvaUdl5NixY8nIyGD69OlcdtllVFRUMH/+fLZu3crw4cP9EaOIiIiIiIiISJfUnuU1bppVeGr26iJsx78B/JuMBDAFRQGQu3I2R16dTEXmSr/erzfzujISIDo6mvvuu8/XsYiIiIiIiIiIdBsOWwW2or0ABLenTds9M7I4E6ejDsPUrjRNj1VT36JtjU3FHJbgt/tUZK6kNm+L6xNHHbaCneStWkjCnDcIT5nnt/v2Vl5/l+/YsaPZ44ZhEBISwqBBg7TIRkRERERERKSLqshcSdGmB6krysASm0rs1CVKuLRTbX464MQcloQ5PNHr8y2RAzEsoTjrqqgrPYA1JsX3QXZj7uU1wf3O9ut9ijY9CBiAe3mNEzAo3vSQfjb8wOs27QkTJjBx4kQmTpzIhAkTPJ9PmDCBUaNGER0dzY033kh1dXWr17r//vsxDKPBx6hRozyPV1dXs3jxYuLi4oiIiGDBggUcO3aswTUOHTrE7NmzCQsLIyEhgV/84hfU1dV5+7JEREREREREeryKzJXkrVqIrWAnTnuNpwJMLant45kXmTChXecbhglrzAhAcyOb41le4+dkZF1RBicSkW5OLRbyE6+TkStXrmTEiBE899xzbN++ne3bt/Pcc88xcuRIXnvtNV544QX+97//sWTJkjZdb8yYMeTk5Hg+Pv/8c89jd955J//973956623+PTTT8nOzmb+/Pmex+12O7Nnz6a2tpYNGzbw8ssvs3z5cpYuXertyxIRERERERHp8U5VASbeOzEvcly7r+Fp1VbiqwFHXTU1xzYDENLfv8lIS2wqrp+LkxmeBUPiW163aT/00EM8+eSTXHTRRZ5jaWlpDBgwgF//+td8+eWXhIeHc/fdd/OHP/yh9QAsFpKSkpocLykp4YUXXuC1117jvPPOA+Cll17itNNOY9OmTUydOpU1a9awa9cuPvroIxITE5kwYQIPPPAAv/zlL7n//vsJCgry9uWJiIiIiIiI9Fi2wm9RBZjv1NRXRrZnXqSbe4lNrZbYNFB7bDPYazGHJWGJHubXe8VOXULeqoWNjjqJmdq2QjvxjteVkenp6QwePLjJ8cGDB5Oeng64WrlzcnLadL29e/fSr18/hg0bxrXXXsuhQ4cA+Prrr7HZbFxwwQWe544aNYpBgwaxceNGADZu3EhaWhqJiSfmMlx00UWUlpbyzTfftHjPmpoaSktLG3yIiIiIiIiI9GQV+/4DDlszj6gCrD2c9YtOoH2btN3c773atBuqPuqaFxnS/2wMo3HVom+Fp8wjYc4bWKKGug4YFuJnv0F4yly/3re38joZOWrUKB5++GFqa2s9x2w2Gw8//LBn3uPRo0cbJAhbcuaZZ7J8+XLef/99nn32WbKysjjnnHMoKysjNzeXoKAgYmJiGpyTmJhIbm4uALm5uU3u4/7c/Zzm/P73vyc6OtrzMXDgwDa9dhEREREREZHuqHz3PxpVfp2c3FEFWHvYivbgtFdjWCOwxAxv93XUpt28zlpe4xaeMo8BN6ZjBEWCsw5LRP9OuW9v5HWb9jPPPMOll17KgAEDGDfONRMhPT0du93OqlWrANi/fz8//elPW73WxRdf7PnvcePGceaZZzJ48GDefPNNQkNDvQ2tze69917uuusuz+elpaVKSIqIiIiIiEiPVLr9rxz/5DYAIk67ntChF1O8aVl9y7ZB/Ox/qgKsHTzLa+LHYRhe13p5uNu0HVUF2KuOYw6N80l83ZnTYacm29UV6+95kSczzEGEDb6Iir1vU7n/v4Qkn9Fp9+5NvE5GnnXWWWRlZfGPf/yDjAzXPIMrrriCa665hsjISACuv/76dgUTExNDamoqmZmZXHjhhdTW1lJcXNygOvLYsWOeGZNJSUl8+eWXDa7h3rbd3BxKt+DgYIKDg9sVo4iIiIiIiEhXVpG5kqJND1JXlIERHIuj0tU5GDVhMX1mPIZhmAhPmceBZ6LBXktwwqQAR9w91eS5k5Htb9EGMFnDMUcOxF52GFtRBubQab4Ir1urPb4TR20JRlAkQX3TOvXeYcPn1CcjV9Hn7Ac69d69hdfJSIDIyEh+/OMf+zoWysvL2bdvH9dffz2TJ0/GarXy8ccfs2DBAgD27NnDoUOHmDbN9YM5bdo0HnroIfLy8khISADgww8/JCoqitGjR/s8PhEREREREZGurCJzZX07tmtjtrM+ERmWMp8+Mx73zN4zTGas0cOwFX6LrXgv1ughAYu5u6r1wfIaN2vsSE8yMqSfkpE12RsACEmaimFqV+qq3UKHXAyGGdvxb7CV7Mfq5+U5vVG7vqJ79+7lk08+IS8vD4fD0eCxpUuXtvk6P//5z/ne977H4MGDyc7O5je/+Q1ms5mrr76a6OhoFi1axF133UWfPn2IioritttuY9q0aUydOhWAWbNmMXr0aK6//noeffRRcnNzWbJkCYsXL1blo4iIiIiIiPQ6RZsexJ2IPMGgrjizyRIQa8wIbIXfUlecCYMv7Mwwuz2n00lt/jag45WRAEGxqVQf+qi+dV7cy2uC+5/V6fc2h8QS0v8cqo+spXLfKqIn3d7pMfR0Xicjn3/+eX7yk5/Qt29fkpKSGvyPmWEYXiUjjxw5wtVXX83x48eJj49n+vTpbNq0ifj4eAD++Mc/YjKZWLBgATU1NVx00UX8+c9/9pxvNptZtWoVP/nJT5g2bRrh4eHceOONLFu2zNuXJSIiIiIiItLt1RVl0DARCeBsdjmKNXYEALaivf4PrIexlx/BUV0IJgvWuI53ZmqJzQlOp9OzvCak3/SAxBA2bI4rGblfyUh/8DoZ+eCDD/LQQw/xy1/+ssM3f/3110/5eEhICM888wzPPPNMi88ZPHgw7777bodjEREREREREenuLLGp2Ap20rgy0ho7sulzY5SMbC93i7a1zyhMlpAOX8+9xMZWlNHha3V3daUHsZcfBZOF4KTTAxJD2PA5FK77OdVHP8NeXYg5pE9A4uipvF73VFRUxBVXXOGPWERERERERESkA2KnLqFxIhKcxExd0uS5nsrI4sxOia0nqcnbBkCwD1q0AU+y2FayH6fd5pNrdlc19VWRwQmTMFnDAhKDNXoY1rgx4LRTdeD9gMTQk3mdjLziiitYs2aNP2IRERERERERkQ4IT5lHcP/61lbDTFDfNBLmvEl4ytwmz3UnI+tKs3Daazsxyu7PXRkZ5IPlNQDmiP4Y1nBw1GEr2e+Ta3ZX7nmRIf0D06LtFjZsDgCV+1YFNI6eyOs27ZSUFH7961+zadMm0tLSsFqtDR6//Xb10ouIiIiIiIgEgtPppK54HwBJ898jdODMFp9rDkvCsEbgtJVjK8kiqE/TVm5p3olkpG8qIw3DwBqbSm3eVmxFe3r118I9LzK4X+cvrzlZ+PBLKfnqESoPfoDTXothDgpoPD2J18nI5557joiICD799FM+/fTTBo8ZhqFkpIiIiIiIiEiA2IoysFfkYJiDCU6eesrnGoaBNSaF2vxt1BXv7dUJMG/Yq4upKz0A+C4ZCa5W7dq8rdgK98Bwn122W7FXHcdWuBuAkAAnI4MSJ2MOS8JemUvVkU8J08Z5n/E6GZmVleWPOERERERERESkg6oPrwVcVWVtWaxijR1Bbf42LbHxgrsq0hI5GHNIrM+uqyU2UJ29AXAtBjKH9g1oLIZhImzYbMp2vkDl/lVKRvqQ1zMj3Wpra9mzZw91dXW+jEdERERERERE2qnqyCcAhAyY0abnW2NSAC2xaauKzJXkv38DAPaaIioyV/rs2tb6ylRb0R6fXbM7qchcScFHPwLAXnHMp+9te4UN/x4AlftX4XQ6W3m2tJXXycjKykoWLVpEWFgYY8aM4dChQwDcdtttPPzwwz4PUERERERERERa53Q6qD7sGqcWOvDcNp1jcW/UVmVkqyoyV5K3aiH2ihwAnLWl5K1a6LOkWZB7o3bhnl6X+HK/t46qAgAcNcU+fW/bK2TguRiWMOxlh6nN3xbQWHoSr5OR9957L9u3b2ft2rWEhJwo+b7gggt44403fBqciIiIiIiIiLSNrWAnjurjGNZwghOntOkca0x9MrJYycjWFG16EDAaHTUo3vSQT67vSgwbOGqKPEm53qLpe+vEl+9te5ksoYQOvgBwVUeKb3idjPzXv/7Fn/70J6ZPn45hnPhGGTNmDPv27fNpcCIiIiIiItJzrUjPYcJjawn71WomPLaWFek5gQ6pW6s6vBaAkH7TMczWNp1jra+MtJcfxWGr8FNkPUNdUQauJNnJnD5rqzZZQrFEDQZ6X6u2rXAP/nxvOyJs2KWAkpG+5HUyMj8/n4SEhCbHKyoqGiQnRUREREREpHc7VbJxRXoOl7+8mfScMqrrHKTnlHH5y5uVkOyAave8yIEz23yOOaQPppA4QHMjW2OJTaW5ykhrrO+2kLuv1VuW2DidTsp2vgQOWzOP+va9ba+woRcDBrV5W6krOxLocHoEr5ORU6ZMYfXq1Z7P3QnIv/3tb0ybNs13kYmIiIiIiEi31VKy8Ydvbufu/3zD91/fBpyohXIChgEPrOkdSRhfczrqqDryGdD2eZFu7iU2dZobeUqxU5fQsHrPAJzETF3iu5sYrjRNwceLOfLq5IDPTPQnh62SgjW31C+tcb+vxkl/+vi9bSdzWDzB/Vz5LlVH+obF2xN+97vfcfHFF7Nr1y7q6up48skn2bVrFxs2bODTTz/1R4wiIiIiIiLSzSxbs6c+neDi/vNvXx5q8RynE/bkl/s7tB6pNm8bztpSTMExBMWP9+pca+wIanK/UGVkK8KGz8UUHIujpghMVoL6nEbM1CWEp8z1yfUrMldSdeA91ydOO7aCneStWkjCnDcIT5nnk3sEUkXmSoo2PUhdUQbmqME466qwlx0Gw0TstN9iiR1ByRe/w1a0B2vsSJ++tx1liRpKTfYGjn/yM0rTnyd26pIe8TUJFK+TkdOnT2fbtm08/PDDpKWlsWbNGiZNmsTGjRtJS0vzR4wiIiIiIiLSzWTkVzSZAAdgMuD2c4bx5raj5JTWNKwzM2BkQkRnhdijVB2ub9Hufw6GyezVuVZt1G6T2oIdOGqKMCxhDP7xMQxLsE+vf2KJS4N6YYo3PdTtE1/ubdnu11dX34ZuBEWR+L23Ca0fLRAxYn7AYmxJReZKKr79R/1njh6XJA4Er5ORAMOHD+f555/3dSwiIiIiIiLSQ6TGh7Mjp6zBMcOAtOQoHr90DNOH9uHylzefeAxXZeTSC1M7OdKeofrwWgBCvGzRBrDUt2lro/apVWW9D7hmcvo6EQn+X5ATSE0TrS6WiP6eRGRX1ZOTxIHi9czILVu2kJ6e7vn83//+N3PnzuX//u//qK2t9WlwIiIiIiIi0j39eNqQBp8bRsNk4/y0ZN6+cQpJka6kTmSIhXdunMK8tOTODrXbc9prqc5eD9CuxI41pr4yUm3ap1R58AMAwoZ81y/X74wFOYHSfKIV6kr2d34wXurJSeJA8ToZ+aMf/YiMDFc57f79+1m4cCFhYWG89dZb3HPPPT4PUERERERERLqf8lo7AOFBZkIsJsYlRTVJNs5PS+atG6YAEGw2MXdsUkBi7e5qcr/CWVeJKTQea9wYr893L7BxVBVgry70dXg9gr26mJrsjQCEDrnIL/douiAHusoSl45yJVob6x6J1p6cJA4Ur5ORGRkZTJgwAYC33nqLGTNm8Nprr7F8+XLeeecdX8cnIiIiIiIi3dBb27MBeHTOaCofns3Wu2c0W/U4ZWA0wRYT+RW17C2o6OwwewT3vMjQATMwjMZJk9aZgiIwh/cDwFak6sjmVB/+GJx2rLGpWKOH+uUe4SnzSJjzBpbIwa4D5mAS5rzZZZa4dERsk4Rq19mW3ZoTSeKTf7a6R+xdldfJSKfTicPhAOCjjz7ikksuAWDgwIEUFBT4NjoRERERERHpdrKOV/LV4WJMhqv68VSCLWbOGBgDwGf7VZXXHtVHPgXaNy/Szb3Epk5zI5tVecDVoh3qpxZtt/CUefS7ZhNggL2GkORpfr1fZ7H2GX3iE3MwQX3Tuk2i1Z0ktvY9sbQ59uyHukXsXZXXycgpU6bw4IMP8ve//51PP/2U2bNnA5CVlUViYqLPAxQREREREZHu5e0drqrIGcPiSIxsfdHH9GF9AFifpWSktxx1VVTn1LcPd2ARyIm5kUpGNuZ0Oqk6sAbw37zIk5lD4wiKHwdA1ZG1fr9fZ6jY8zoAoUMuZuhtZfS/bnO3SuaFp8xjwHWbCR3qKsgzLKEBjqh78zoZ+cQTT7BlyxZuvfVW7rvvPlJSXLMl3n77bc466yyfBygiIiIiIiLdi7tF+4rx/dr0/OlDXMnIzw8oGemtmuyNYK/FHDHAsxW7Payx9Ru1i5SMbKy2YAf2imwMSxgh/c/plHu6q1zdW9K7M6fTSfm3rmRkxKirAhxNxwQnnQ5AzbGvAhxJ92bx9oRx48Y12Kbt9v/+3//DbDb7JCgRERERERHpnvYfr2DzkZI2tWi7TRvSB8OAzIIKckurSYoK8XOUPYdnXuTA9s2LdLNoo3aLqrLeByBk4EwMS+uVvr4QOnAmpVueoOrw2k65nz/VHttMXck+DEsYYcO+F+hwOiQ40bVwq/bY5gBH0r15XRl5+PBhjhw54vn8yy+/5Gc/+xmvvPIKVqvVp8GJiIiIiIhI9/L2jhwAZg7vS0IbWrQBYkKtjEuOAuBztWp7xTMvckD750XCiZmRtqK9OJ2NNzr3bpUHXfMiO6NF2y2k33QwzNSV7KOu9FCn3dcfyr/9JwBhwy/FFBQR4Gg6xp2MtBXtxV5dHNhgujGvk5HXXHMNn3zi+s1Lbm4uF154IV9++SX33Xcfy5Yt83mAIiIiIiIi0n2caNFuW1Wk29lq1faao7aMmlxXu2jIwBkdupY1ehgYJpy2cuyVub4Ir0ewVxe7WuGB0CEXddp9TcFRBCdOBrr33Eino46KjLeA7t+iDa55npboYQDU5n0d4Gi6L6+TkTt37uSMM84A4M0332Ts2LFs2LCBf/zjHyxfvtzX8YmIiIiIiEg3sf94BV/Xt2jPG+tdMnL60PpkpDZqt1n10c/BaccSPRxr1OAOXcswB2GJGgKArUit2m7Vhz8Gpx1rbCrW6KGdeu+Q+oVE3XluZNXhT7BXHsMUEkfooAsDHY5PuKsj3b8IEO95nYy02WwEB7tK7T/66CMuvfRSAEaNGkVOTo5voxMREREREZFu463trn8TnpvS9hZtN3cyclt2CWXVdT6PrSfytGh3sCrSzb1Ru04btT0qD7hatEM7sUXbLbR+iU3V4bXdtnW+on5xTXjq5RjmnjHaLzipPhmpuZHt5nUycsyYMfzlL3/hs88+48MPP+S733X9QGZnZxMXF+fzAEVERERERKR7cLdoXz7Ou6pIgAExoQyJDcXhhI0HVR3ZmorMlZRs+zMAVQc/pCJzZYeveWKjdkaHr9UTOJ1Oqg6sATq3RdstOHkamKzYy49QV7Kv0+/fUY66Kir2/QuAiFFXBzYYHwpOrN+onatkZHt5nYx85JFH+Otf/8rMmTO5+uqrGT9+PAD/+c9/PO3bIiIiIiIi0rvsK6hgy1Hvtmg3ds4wV4GLlticWkXmSvJWLQR7NQD2siPkrVrY4YSkVRu1G6gt2IG9IhvDEkZI/3M6/f4maxghyVMBuuVW7ar9q3HWlmGJHExw/evoCYISJoBhxl6RTV350UCH0y15nYycOXMmBQUFFBQU8OKLL3qO//CHP+Qvf/mLT4MTERERERGR7uGtHa6qyPNS+hIf4V2LttvZ9a3a65WMPKWiTQ8CxklHnIBB8aaHOnRdS4y7MlJt2gBVWe8DrtmNJktIQGI4MTfyk4DcvyPcW7TDRy3EMLxOP3VZJms4QXGjgbZXR1ZkruTIq5M58HQkR16d7JNK5u6sXd8NZrOZ2NjYBseGDBlCQkKCT4ISERERERGR7uXt+nmRl4/v1+5rTK/fqL3pUBG1dQ6fxNUT1RVl4EpAnsyJrWhPh65rja2vjCzZh9Nh79C1eoLKg655kWEBmBfp5pkbeeTTbjU30l5dROUBVzI3YmT336LdWFBSfav2sdaX2LgrmW0FO3Haa7AV7PRJJXN31q5k5Ntvv82VV17J1KlTmTRpUoMPERERERER6V0y61u0zSaDeWOT2n2d0xIjiAuzUmVzsPVoiQ8j7FkssanNHDWwxo7s2HUjB4E5COy11JUd7tC1ujt7dTE12RuBwMyLdAtOPB3DEoqjMg9b4a6AxeGtir0rwGEjqG8aQX3HBjocn/Ns1D72davPPVHJ7E4m+6aSuTvzOhn51FNPcfPNN5OYmMjWrVs544wziIuLY//+/Vx88cX+iFFERERERES6MPfimnOHx7W7RRvAMAxPq7bmRrYs5oz/a3TEleiImbqkQ9c1TGas0cMAsPXyjdrVhz8Gpx1rbCrW6KEBi8OwBBPc72yge82NrNhTv0W7By2uOZk7GVmbuxmn89RV3P6qZO7OvE5G/vnPf+a5557j6aefJigoiHvuuYcPP/yQ22+/nZIS/eZKRERERESkt3m7fl7kFR1o0XY7e4g7GXm8w9fqqYL6uCsgTWAOJqhvGglz3iQ8ZW6Hr+1eYlPXy+dGVh5wtWiHBrBF2y20m82NrCs7QvWRdQBEpF4Z4Gj8IyhuDIY5BEdtCXWtLHxyVTIbjY52vJK5O/M6GXno0CHOOussAEJDQykrKwPg+uuv55///KdvoxMREREREZEua0V6Dqc98j+2Hi0FwGpu/A9u7528Ubs7zcjrTO7W0JD+0xl6Wxn9r9vsk0QknDQ3shdv1HY6nVQdWAMEtkXbLWTATACqj3zWLWZ5VmS8CTgJ6X8OlqhBgQ7HLwyzlaCEiQDU5J56bmTs1CU0rIz0TSVzd+Z1MjIpKYnCQle5/KBBg9i0aRMAWVlZ+j8KERERERGRXmJFeg6Xv7yZPfkVnmPff2M7K9JzOnTdSf2jCbGYOF5p49u88o6G2SPVHHNt8A1OmuLza1vqKyN760btisyVHHl5DPaKbMDAUV0U6JAITpyEERSJo6aI2oIdgQ6nVZ4t2j1wcc3J2jo3MnTg+WCyuD4xBfm0krm78joZed555/Gf//wHgJtvvpk777yTCy+8kIULFzJv3jyfBygiIiIiIiJdz7I1e5o2HhrwwJqMDl03yGLizEGxgOZGtsSdjAxKnOzza5+ojOx9yUj31uMTbbdO8t+7LuBbjw2ThZD+5wBdu1W7InMlh5ePoTZ/OwCGuf3zY7sD9y8DWquMrNj/H3DUYY1NZYiPK5m7K6+Tkc899xz33XcfAIsXL+bFF1/ktNNOY9myZTz77LM+D1BERERERES6noz8iqYrGZywJ7/j1YzTh7nmRq5XMrIJZ10Ntfmu6jh3ZZYvuZORdaUHcNprfX79ruzE1uOTdY2tx6EDzwX8v8SmInMlR16dzIGnIzny6uQ2J2JPJHJPJLELPrwl4IlcfwpOOh2A2vxtp/xZqch4G4Dw1CsxjI6PsugJvE5GmkwmLBaL5/OrrrqKp556ittuu42goCCfBiciIiIiIiJdU2p8eLOVkSMTIjp87en1G7U/UzKyidrjO8FhwxQShyVqiM+vbw5LwrBGgNOBrWS/z6/flXXlrcchA2YAUH30c5x2m1/u4U4o2gp24rTXYCvYSd6qhW1KKHblRK6/WKKHYwqOxWmvofb4N80+x15dSNVB1/zR8JFXdGZ4XZrXyUiAoqIi/vCHP7Bo0SIWLVrEY4895pkjKSIiIiIi0pWsSM9hwmNrCfvVaiY8trbDMw3FZemskQ1XMhiuysilF6Z2+NrTBsdiMiCrsJKjJVUdvl5PUpNbPy8ycbJfqqwMw8AakwL0vrmRrq3HjXWNrcdB8eMwhfTBaStvdUZhe7kSinAiIeukrQnFrpzI9RfDME7MjWyhVbsy89/gqCOobxpBfU7rzPC6NK+TkevWrWPo0KE89dRTFBUVUVRUxFNPPcXQoUNZt26dP2IUERERERFpF/eSlfScMqrrHKTnlHH5y5uVkPSB+WnJjKqvgrSaDMYlRfHOjVOYl5bc4WtHhVgZ3y8KgPVZgV8g0pV4ltf4YV6km6dVu5dt1A7pN73Rka6z9dgwTCeqI4/4fm6ko64aW7PVfW1LKLoXHzXUNRK5/hSU5Po5dP9cNlae8SbgatGWE7xORi5evJgrr7ySrKwsVqxYwYoVK9i/fz9XXXUVixcv9keMIiIiIiIi7eJestKgzscHS1YEauscHCisBGDHz2ey9e4ZPklEup09NA6Az7KO++yaPcGJ5TW+nxfp1hsrI+3VRVTsfQsAc3gyhjm4y209PjE38lOfXreu/Ci5b50PTkczj7YtoRg66Lwm53WVRK4/BSe65kY2VxlprzjmWTikFu2GLK0/paHMzEzefvttzGaz55jZbOauu+7ilVde8WlwIiIiIiIiHdHSkpXdeWUBiacn2Z5dSnWdg9hQK6nx4T6//jlD+/Cnz7O0xOYkDlsFtsLdwIlNvv5g6YUbtYs23o+jqgBrn1H0v/ZrDLM10CE1ETKwvjLy8CdkPR2JNTaV2KlLCE+Z1+5rVmdvJG/Vldgrj2FYI3DayqHRr3BaSyg6HXaqDn4IgCksEWdNMdbYkcRMXdJlErn+4v45tBXuxlFbjinoxMzcisyV4HQQlDgFa/SwQIXYJXldGTlp0iR2797d5Pju3bsZP368T4ISERERERHxheaWrADU2p3c+M+tnso+8d7Gg64k4dTBsX6ZXXj2ENcSmx05pZRU+WdhR3dTm7cVnA7MEf2xhPuuCrUxa4xrdqKtG7Zpt2cbdE3eVsp2/BWAuHOf7JKJSIDa49/W/5cTvFww05zS9BfIefsC7JXHsPYdS//rNpMw5w2sfdNwp4uCk6e2mlCs2PsOtsLdmIJjGHjjTobcVkb/6zb3+EQkgCU8GXPEAHA6qMnb2uAxd4t2xEi1aDfmdWXk7bffzh133EFmZiZTp04FYNOmTTzzzDM8/PDD7Nixw/PccePG+S5SERERERERLy2dNZLLXz4xy+vkep+/f32EN7ZlM2tkPPuPV7D/eCWp8eEsnTWS+T5sN+6pNh10zXKcOjjWL9fvFx1CYkQQx8prSbj/A05LiOj1Xxv34pLghEl+vY97RqC9/ChH/j6R2GlLO1R911nc26DdP+nuZF3CnDdajN/pdHD8kzvA6SA89QpPK3RXVPzFg42OnFgw05avT0XmSoo2PYitKAOTNRxHtesXCmEj5hN/4d8wBUVgjR5GeMo8avN3cPS1M6jJ2UT10c8J6d94nmZ9BE4HxV/+DoCoibdhCo7uyEvsloITJ1NZfoTaY18ROuAcAOrKjlBzdD0A4SMWBDK8LsnrZOTVV18NwD333NPsY4Zh4HQ6MQwDu93e8QhFRERERETaaeqgWE8CMshsqk9opTIwJpRfrd7N/zILWLXrmOf57gU3b984pVcnvdrCnYyc5qdk5Ir0HI6V1wJgszv1teGk5TV+bNGuyFxJwZpFns9tx3e1mtDrKlzboJtMiT1lsq5816vU5GzCsIbT55xHOinS9nFtrG6sbQtmGidqHfYaAMJHLiT+u680qW4Oih9H5NhFlKU/z/FP76bf1RsxjKbNtZV7V2A7vgtTUDRRE25rx6vq/oKTTqdy3789m+7BVS0KToL7nY0lcmDgguuivG7TzsrKOuXH/v37PX964+GHH8YwDH72s595jlVXV7N48WLi4uKIiIhgwYIFHDt2rMF5hw4dYvbs2YSFhZGQkMAvfvEL6urqvH1ZIiIiIiLSA/1jyxGcwNlDYql+ZLZnycqUgTF8+KOpDO0T2uD5WnDTNrml1RwoqsIw4IxBMX65h3v5kJu+NicqI/25vOZEQs/tREKvq3Ml65pMiaW2hWSdvbqYws/vBSDmzPuwRA7wb4AdZIlNhSaDJ9q2YKZpotZ1ru347hbHLMRO+w1GUBS1eVsp3/X3Jo87nQ6KvnB9X0RNvBVzSExbXkaPE5xUv8TmpI3aFXvUon0qXldGDh482OdBfPXVV/z1r39t0tZ95513snr1at566y2io6O59dZbmT9/PuvXu0pd7XY7s2fPJikpiQ0bNpCTk8MNN9yA1Wrld7/7nc/jFBERERGR7sPpdPLK5iMAXD+laWWKYRjklNY0cx7syS/3e3zd2cb6qsgxiZFEhfhnvl5Ly4d669fGXl1EXf0Mx+DEyX67T0sJvbZU3wWaJTYVW8FOmsTvsFG2+1UiRl3bIPFWvPF+HFX5WGNHEj3x9s4Nth1ipy6pr250a/vG6vZ8Xc1hCcSe+X8UfvYritb/mvAR8zEFRXoer8z8F7bj32AERRHVDd4/f3GPTagrPYC9Mh+HrZyaY1+BYSI8ZX6Ao+uavK6M9LXy8nKuvfZann/+eWJjT5T3l5SU8MILL/D4449z3nnnMXnyZF566SU2bNjApk2bAFizZg27du3i1VdfZcKECVx88cU88MADPPPMM9TW1rZ4z5qaGkpLSxt8iIiIiIhIz7Itu5RvjpURbDFx5fh+zT6nuQU3hgEjEyKafb64bPTzvEjQ16ax2vqqSEv0cMwhffx2n45U3wVa7NQlNK78A8DpoOCD75P33wWU7nyJI69OJuupCEq3/xlwL60J6vR4vRWeMo+EOW9gDnf975lhCSVhzpttWhTT3q9r1IRbscSkYK/MpfirRz3HnU4HxfVVkdETFmMO8d//FnR1puBoz/tYc2wzFRlvARAyYCbm8MRAhtZlBTwZuXjxYmbPns0FF1zQ4PjXX3+NzWZrcHzUqFEMGjSIjRs3ArBx40bS0tJITDzxxb3ooosoLS3lm2++afGev//974mOjvZ8DByo/n0RERERkZ7m5c2HAbhsTBIxoc1X7y2dNdLT/uvmdMLSC1M7IcLu6ws/z4uEE1+bk/Xmr41nXqQfqyLh5IRew1bttlTfBVp4yjzChn3P9YlhJqhvGvGz/0nsWcvAZKVy/yqOf/QjV/Wk40QBk6O2JEARey88ZR79rtkIGDjrKtv8/dB8orb1r6thDqLPOQ8DULrlCWwlBwCo3PcfagvSMYIiiZp0h9evo6dxz3GtObZZLdptENBk5Ouvv86WLVv4/e9/3+Sx3NxcgoKCiImJaXA8MTGR3Nxcz3NOTkS6H3c/1pJ7772XkpISz8fhw4c7+EpERERERKQrsdkd/HPLUQCun9zyHLj5acm8feMUxiVFYarPvVw+Lpl5vXRBSlvY7A42HykGYNoQ/yUj3V+bAdEhAIQHmXnnxim99mvj2aTt52Sku/ouqG8a1C8sCR32vTZV33UFjhpXYrHvBc/S/7rNRIxYQMwZv6L/NZvAHFL/rIZJue4wD/NklvBkgvufDUDF3pVtOids2PcwgqJcn5isBPVNa3NVZdiw7xEy8Fyc9hqKPr8Xp9PpqYqMGv9Tv1bqdhfBia65kRV73qC2YAeYLIR1k5+ZQAhYMvLw4cPccccd/OMf/yAkJKT1E3woODiYqKioBh8iIiIiItJzfLAnn/yKWhIigrhoZPwpnzs/LZmtd8/ghSsnALDveEUnRNh9bc8upcrmIDbUSmpf/7ZMz09L5uMfTwNcG7W/OyrBr/fryjpjeY1beMo8+l+3mYRLXgPAdvwbnM7Gdapdj9NhpyZvCwDBjd6noL5pNJ2ZCN1lHmZj7lmEFZkr2vT86qOf4awtxRQSx5DFxfS/bnObE8yGYRD3nf8HhomKve9w6Ln+1OZvB0xYY0a08xX0LO6fS1v9xvPQQRcqSXsK7UpGFhcX87e//Y17772XwsJCALZs2cLRo0fbfI2vv/6avLw8Jk2ahMViwWKx8Omnn/LUU09hsVhITEyktraW4uLiBucdO3aMpKQkAJKSkpps13Z/7n6OiIiIiIj0Pq/Ut2hfM2kAFnPb/tlz8agEDAO2Hi3laEmVP8Pr1tzzIs8cFIPJ1PwWXl9K6RtO/+gQau0ONh4o8vv9uqK6ilzs5UfAMBGcMLHT7hs65CIMcwh1Jftd1V5dnK3oW5y2cgxrONY+pzV53NqN52E2Fj5iHgA12RuoK289F1OR8TYAYSmXYZi9XzoVFD+OkAHnAuCoKqg/6qDgw1uoyGxbdWZPFhQ/Dgyz5/PagnS9L6fgdTJyx44dpKam8sgjj/CHP/zBkyxcsWIF9957b5uvc/7555Oens62bds8H1OmTOHaa6/1/LfVauXjjz/2nLNnzx4OHTrEtGmu34xNmzaN9PR08vLyPM/58MMPiYqKYvTo0d6+NBERERER6QGKKmv5zzeuIoUbTtGi3VhCZDBnDnK1Ha/endfKs3uvLzzLazqn6scwDGYOjwPgk30FrTy7Z6qtnxdp7TMKU1DnLfAxWcMJHTILcG1O7upqcuvnaiZMwjCZmzzedB5m27dRdzWWiP4EJ7tyIxWtfG2cjjpPYixixOXtvqe9IruZo92vzd0fqg68B06753N7+VHyVi1UQrIFXicj77rrLm666Sb27t3boL36kksuYd26dW2+TmRkJGPHjm3wER4eTlxcHGPHjiU6OppFixZx11138cknn/D1119z8803M23aNKZOnQrArFmzGD16NNdffz3bt2/ngw8+YMmSJSxevJjg4GBvX5qIiIiIiPQAb27PptbuYFxyFBP6R3t17uzTXG3Aq3cda+WZvZe7MtKf8yIbmzm8LwBrM3tnMvLE8hr/t2g3Fjb8MqD1hFdX4Hmfkk5v9vGT52Ea5mCv5iZ2ReEjFgBQsfedUz6v+sg6HFUFmELiCBk4s933qyvZ38zR7tnm7mtFmx5sdMSV9FaitnleJyO/+uorfvSjHzU53r9//1MujWmPP/7xj8yZM4cFCxbwne98h6SkJFasODEPwWw2s2rVKsxmM9OmTeO6667jhhtuYNmyZT6NQ0REREREuo9XNh8BTr24piVzRrsWYn60N58qm72VZ/c+x8pqyCqsxDDgjIExnXbfc1NclZFfHi6moqau0+7bVdTkds7ymuaEDZsNJgu2499gK9rb6ff3Rk3uV8Cpk7bueZhDbivzam5iV+Rp1T66nrqKnBaf527RDk+Zi2GytPt+lh7U5u5rdfWzIhtSorYlXicjg4ODKS0tbXI8IyOD+PhTD4Zuzdq1a3niiSc8n4eEhPDMM89QWFhIRUUFK1asaDILcvDgwbz77rtUVlaSn5/PH/7wByyW9v9wiYiIiIhI97U3v5yNB4swGXDNpP5enz8uOYoB0SFU2Rx80kur8E5l40HXzoDRCZFEh3o/d669hvYJY2BMCDa7kw0He9fcSKfTSU2eOxnZ+ZWR5pA+hAyYCUDFvn93+v3bylFX7Zlr2RlLfroCS+RAgpPOBJwtttE7HXVU7HM9Fp7a/hZt6Flt7r6mRK13vE5GXnrppSxbtgybzQa45nccOnSIX/7ylyxYsMDnAYqIiIiIiLTV3792VUVeNDKB5KiQVp7dlGEYzK6vjlylVu0mNh0sBmBqJ7Zog+vrcq67VbuXzY2sKz3oWhhishLUd1xAYnBXD3bluZG1+dvBUYcpNB5L1OBAh9NpwkfUb9Xe2/xW7eojn7patEP7EjJgRsfu1cPa3H1JiVrveJ2MfOyxxygvLychIYGqqipmzJhBSkoKkZGRPPSQeuFFRERERCQwHA6nJxnZnhZttzmnuZKRq3cfw+l0+iS2nmJTfWXktMGdm4wEmFG/xGZt5vFOv3cguZfXBPVNw7AEZjdC2PDvAQY1uV9SV3YkIDG05uS5mobh/y3vXUVYiqtVu/roZ9grmv4CxdOiPbxjLdpuPanN3ZeUqPWO19+J0dHRfPjhh3z++efs2LGD8vJyJk2axAUXXOCP+ERERERERNrks6zjHCyqIirEwmVjk1o/oQXnjehLqNXE4eJq0nPKGNcvyodRdl82u4OvDhcDgUlGnpviqoz86nAx5TV1RAT3jvFcgVxe42YJTyY4eSo1ORup3Pcfoib8NGCxtMQzLzKpd7Rou1mjhxCUOIXaY5up2Pdvosb90POYa4v2v4COt2hL68JT5hFenxyWU/O6MtJt+vTp/PSnP+Wee+5RIlJERERERAJmRXoOEx5bywV/2QjA6QNjCLWa2329UKuZ81Nc8/BX7VarttuOnFKqbA5iQq2MjI/o9PsP6RPGkNhQ6hxO1h8o7PT7B0rNscAtrzmZu8Krq86NrD3mXl7T/CbtnqylVu3qw2txVB+vb9H+TgAiE2leu36V9PHHH/Pxxx+Tl5eHw+Fo8NiLL77ok8BERERERERasyI9h8tf3lw/ncvl470FrEjPYX5acruvO2dMIqt2H2P1rmP83/kjfBJrd7fxgGtxzJmDYjCZAtMGO3N4X5ZvPswnmce5aGRCQGLoTE6ng5q8LUDgk5FhKZdR+NkvqT6yDntVAebQvgGN52T26mLPpu/eVhkJrmRk0ef/R/WRtdgr8zGHuX6ZUrH3HdfjKfN80qIt4iteV0b+9re/ZdasWXz88ccUFBRQVFTU4ENERERERKSzLFuzp0EiEsAw4IE1GR267uzTXImuTYeKyC+v6dC1eoovDrn+vTc1AC3abjNTXHMjP+0lS2xsRRk4a8swLKFY40YHNBZr9DCC4seD007l/tUBjaWx2vpt45aooV0qSdpZrNHDCEqYCE4HFfv+A4DTblOLtnRZXqfG//KXv7B8+XKuv/56f8QjIiIiIiLSZhn5FTReMeN0wp788g5dt390KBP7R7H1aCnvfZvHDVMGduh6PcHGg65kZCDmRbrNrF9is/lICWXVdUSG9OxqL/e8yKCEiV2isi0sZS61+dupyFxJ5JgbAx2OR2+dF3my8BELqM3bSsXed4hKW0TVEXeLdjwh/c8JdHgiDXhdGVlbW8tZZ53lj1hERERERES8khofTuOGYcOAkQkdn2k4271Ve5fmRuaV1bD/eCWGAWcOClwyclBsGMPiwrA7nHye1fO3atd65kV2jSRb+PDLAKg69BGO2rIAR3NCV1jyE2juuZHVhz/BXnVcLdrSpXmdjLzlllt47bXX/BGLiIiIiIiIV5bOGtmkRdvphKUXpnb42nNGu5KRH2TkU1vnaOXZPZu7KvK0hAiiQ60BjWVGfXXkJ/t6fjKyJtedZJsU4EhcrHFjsMSkgL2WygPvBzocD8/7lNT7lte4WWNSPG30FXtXUOlp0V4Q2MBEmuF1ery6uprnnnuOjz76iHHjxmG1Nvw/oscff9xnwYmIiIiI9BQr0nNYtmYPGfkVpMaHs3TWyA4tWBGX+WnJXDOpP69tOYrJgLSkKJbOSmWeD97bKQNiSIgIIq+8ls+zCjlvRO+bRee2yTMvsk+AI4Fzh/flpS8P82kPT0aW73mbmtwvASj64ncYllDCU+YFNCbDMAhPmUvJ5j9QmfkvIlKvCGg8AHXlR7FXZINhcs1N7MXCR8ynNn87RRvvx1FdiCksQS3a0iV5XRm5Y8cOJkyYgMlkYufOnWzdutXzsW3bNj+EKCIiIiLSvbk3PqfnlFFd5yA9p4zLX97MivScQIfWIxwrcy2Y+eNlY9l69wyfJCIBTCaDS+pbtVft7t2t2psOBH5epJt7buTXR4opqbIFOJqWVWSu5MirkznwdCRHXp1MReZKr87Nf+8a3KuZ6or2krdqoVfX8Bd3QrQy6z0cddUBjuZEVaQ1bjQma3iAowms8BGuKkhHVb7rgKOOyv3/DWBEIs3zujLyk08+8UccIiIiIiI9VuONz05ObHxWdWTHVNnsfJ5VCMAFfqhcnDM6keVfHWb1rmM8fukYn1+/O6izO/jqcDHQNZKRA2JCSekbTmZBBZ9lFXra6buSisyV5K1aCPU/+baCneStWkjCnDfaVN1YtOnBRkecgEHxpocCXh0ZlDgZU0gcjurjHPxzHNY+o4idusSncVVkrqRo04PUFWVgiU095fVrjtUvr0nsvS3abrXHv2nwuaO6yKvvO5HO4nVlpIiIiIiIeMdfG58FNhwopKbOQb+oEEb5YGlNYxeOiMdqNthbUEFGL/167cgppdJmJzrE4pf3uD3c1ZFr9xUEOJLmuZKJTX4FQfGmh9p0vq1wTzNHndiKmjveuSr3/RtHdX2LvMPmSbT6qmrTnci1FezEaa9p9fqaF3nCqZLYIl1Jmyoj58+fz/Lly4mKimL+/PmnfO6KFSt8EpiIiIiISE+RGh9Oek5Zk4RkQkRwQOLpST7a60pGXTCiL4bReK92x0WGWJg5PI4PMwpYtesYd83oGsm4tvLFrNJNB4sB1xZtk8n373F7zBwex9++OMTazK45N7KuKAOa/gqiTcnE2vwd4Kxr5hEDa+xIX4TXIadKtPqi+s6b6zudjpM2jk/u8L27O9f3XWNdI4ktcrI2VUZGR0d7/o89Ojr6lB8iIiIiItJQ443Pbtml1XzaRSu7uouPM1yz0c5P9d9ymQHRoQDcs2oXEx5b221mffpiVumK9ByWvLcbgG3ZJV3mtc8c7vp6b80uobgLzo20xDa/zd0SNeSU59UWfkvOiovB6d7ebpz0p5OYqUt8FWK7dSTR6uvr24r24qgtwTCHEBQ31if3785c33eNf2HQNZLYIidrU2XkSy+91Ox/i4iIiIhI6+anJTMyPpw9+RVYTAajEyMIs1rYdKiIecs3s/7WszktMTLQYXY7hZW1fH20BIDzU+L9co8V6Tm89NVhABxOPAm9t2+c0uXnfXZ0Vqk7memWX17bZV57v+gQUuPDycivYN3+41w6Jimg8TQWMeoaij6/t8lxe8UxagvSCeqb1uQxW/E+ct/5Lo6qfILiJxA18XZKtzyBrWgP1tiRxExdQnjK3E6I/tQssanYCnbSMGHou4SXKSwJe9nBZu7b9PrueZFBCRMxzFaf3L87i526pMGs0q6UxBY5mWZGioiIiIj4WUFFDRkFFQDs+7/z2Xb3TD7+yTTOGhJLcZWNS/72Bbmlgd9K2918klmA0wmjEyPoFx3il3u4E3puJyf0urqWZpXuOlaG09lcre4JdXYHd/57Z8Nz6Vqv3V0duXZf12vVdtpc80WNoCgMczDWPqOxRA7CUVtMzlsXUJP7VYPn15UdJved72KvyMYaN5qk+e8SOfo6+l+3mSG3ldH/us1dIhEJroTXiUSXm28SXo6aEpy1pc0+FtS3aeXjiRbtKR2+d08QnjKPhDlvENQ3DcMcTFDfNBLmvNllvndE3NpUGTlx4sQ2z1/ZsmVLhwISEREREelp3v82H6cTxiVHMTDG1fIbajXzr5tP5+yn17O3oILvvfgln/zkLCKC2/RXdOHEvMjzR/inKhK69/KhlmaV2hxOpj31Ob+ffRpFVbYGMyX/7/wRlNXYefh/ezlc3DRB3pVe+7kpcTy36SBrM7veqIPK/asAiJv5OJGjbwDAXl3EsX9dSk3uF+S8cxHRk++iInMltqIM1xvrqMUSk0LS/Pcwh/pv7EBHuRNeRRuXYavf3txn5h99kvAq/Pz/cNQUYQpLwhzWl7qivZhC+mKvOErFt/+kMvVywobN8TzfndQNTlIy0i08ZZ42Z0uX16a/6cydO9fPYYiIiIiI9Fzv7j4GwCWnJTQ43jc8mNW3nMlZT3/O10dK6L/sQ2rtDka2c9FIb/PxSctr/KW5hJ5hwMguslX6VJbOGtmgzdowXDmvYIuJLw8Xc/5fNrqO46pzS88p46pXTxSXmA2wN8pkdqXXPmOYa6P2tuxSQn+1usv83NSVHqI2fzsYJsKGXOw5bg6JJWn+exz77+VUH/4fxZuW0XBRC0RPuhNLeNf/uXcnvHJXzqHq4BqcdVUdvmbVkXWUpT8PQMIlrxI64Duexwr+dxtlO/5K3ns30G/hOoL6jsVpr6UmfxugTdoi3U2bkpG/+c1vALDb7axfv55x48YRExPjz7hERERERHqEOruD9/e4lqzMPi2xyeMpfcO56zvD+L/3vqWsxrVBtzvNJQyUA4WVZBZUYDYZzBge57f7uBN67kQeuP5cemHzC0q6kvlpyYxJjOCbY+VYTAZjEiNZOiuVs4b04YEPM/jzhgNAw5mSABaTwe8vOY3kqGCue22r57W7/+wqr33DwSLPf9ectKAn0D83lVmrAQhOnoY5rGHVrikogsTL/sWh5/rjrC2j8dzFsh1/JWrcDzov2A4KG+ZKRlbuX0XMlJ+3+zqOuioKPvoxAJFpP2iQiASIm/E4tsI9VB9Zy7H/LKDf1eupKz0E9lpMwbFYood36HWISOfyamak2Wxm1qxZFBUVtf5kERERERFh48Eiiqts9AmzMnVwbLPPeX3b0W47lzBQ3FWRZw6KISrEf4sr5qcl8/aNUxiXFIWp/ou0YFwy87pBkrimzs6+45UA7Pj5DLbePYN5ackkRgbzp/lpBJmb/+eg2WRw98zhXDNpgOe1h1hMjEuK4p0bp3SZ175sTcPtyl3l56Zyn6tF++R24pOZLCE47bXNPOK7jdSdJWzYbABqcjZhr8xv93WKNy6jrjgTc0R/+kz/XZPHDbOVhNn/xBI9jLrSLPJWXUV19nrANS+yrWPlRKRr8HqBzdixY9m/f78/YhERERER6XFW784D4OJRCZhNzf+DuTvPJQyUj/e6Eh/npfh/tt78tGS23j2DFxdOAGD/8Qq/39MXvjpcTHWdg4SIIEbGN22tHpUQTuPvSMOAUSe1Ybtfe+XDsz3JzK4iI7/p1yHQPzeOmlKqjqwFWk5GAlhjU6Hpu++zjdSdxRI5kKD48eB0UHngvXZdoyZvKyVbngCg73lPYwqObvZ55tA4Ei9dgREUSfXRdRR+6qrErMnfTkXmynbdW0QCw+tk5IMPPsjPf/5zVq1aRU5ODqWlpQ0+RERERETkhBPzIpu2aLulxjeTFKLrzObrahwO50nzIv23vKaxi0clYBiw9Wgp2SVdf/v5uv2FAHxnWFyzlWNLZ430VBNC12vDbk2zPzc+nGlZkbmSI69O5sDTkRx5dXKbEl5VB9eAw4Y1dgRBfVpOLDbdSO2aHemLjdSdzZ10rdy/2utznXYbBR/+EJx2wlOvOGUCFyAobjRR43/qPhsAR1U+easWKiEp0o14nYy85JJL2L59O5deeikDBgwgNjaW2NhYYmJiiI1tvu1ERERERKQ3OlRUyc7cMkwGXDSy5aRZ46QQuP6Z3V2SQp1tZ24Z+RW1hFnNLba++0N8RDBnDIwB4N1vj3Xafdtr3b7jgCsZ2ZyTW9C7Yht2a9w/NyfzVTK1InMleasWYivYidNeg61gZ5sSXhX73S3a3zvl89wbqYP6pmGYgwnqm0bCnDd9spG6s7kTiFUH1+Coa1uS3pPo/VM0tfnbMawRxM38Y5vOrcx6t9ERV1K3eNNDXkQtIoHUpgU2J/vkk0/8EYeIiIiISI/jbtE+a0gf+oQFtfg8d1LogTUZ7M4ro9buxAAmD2i+XbG3+6i+RXvG8DiCLF7XV3TIJacl8sWhYt7dncctZw7u1Ht7o87uYP2BE5WRLZmfltxtlyS5f25+uWoX+45XYjLgH9dOanMydUV6DsvW7CEjv4LURpu4izY9SMNN1ycSXuEp85q9ntNRR1WWq1W5tWQknNhI3d0FJUzEHN4Pe0U21Uc+JWzIRad8vjvRe/L767SVU529vk3vR11RczNBu9+8TZHezOtk5IwZM/wRh4iIiIhIj7N6l7tFO6HV556cFDr/2Q18su84f9l4kN9dcppfY+yO3C3a54/w/7zIxmaflsBvPtjDhxn51NTZCbaYOz2GtthytISKWjuxoVbGJkUGOhy/mZ+WzNwxSQx56COOlFRjbWEpT2Mr0nNcW9JxpcMab+J2Jbya1F2eMuFVfXQ9jpoiTCFxBCdPbe9L6nYMw0TYsNmUpT9P5f7VrSYjmyZ6obVE78kssanYCnY2Ob+7zdsU6c3a9L/UO3bswOFweP77VB8iIiIiIgKVtXX8L9OVNJt9inmRzbl1+lAA/vbFIaptdp/H1p3V1jn4tL79uDPnRbpN7B9NclQwFbV2z0zGrsgd2znD+mBqYXFST2EyGVw1sT8Ar2050qZzlq3Z07Tu8aRN3JZmF8yAJWZEi9esdLdoD70Ew9Q1k9T+4t6qXZm1GqezcRK3ofYkek/Wk+ZtivRWbUpGTpgwgYKCAs9/T5w4kQkTJjT5mDhxol+DFRERERHpLj7JPE51nYOBMSFeV6Z9b3QiA2NCKKio5Y1t2X6KsHvadLCISpudhIiggFT8GYbBxaNcyeXVu7vu3MjP9rsStucMbblFuye5dpIrGbl6dx7FVbZWn9/aBvumCS8XS+TAZq/ndDpPJCOHn3oJS08UMvBcDEsY9rLD1OZvP+Vzm0/otr2ysSfN2xTprdqUjMzKyiI+Pt7z3/v37ycrK6vJx/79+/0arIiIiIhId7H6pC3azW0yPhWL2cRPzhoCwJ/WZ7VaadSbuOdFnj8iPmAVf7Pr2+5X7zrWJb82doeTz7JclZEzhveOZOS45ChGJ0ZQU+dgRXpOq89PjQ9vcuzkTdyNE16WKFe1ctWB96g6/GmTc22Fu6kr2QfmIEIHXdjBV9P9mCyhhA46HzhRIdqS4H5nNTrifWVjeMo8+l+3mSG3ldH/us1KRIp0M21KRg4ePNjzF6jBgwef8kNEREREpLdzOp28W7+8xtsWbbdbzhxEsMXE10dK2HSwyJfhdWuBnBfpdsGIeKxmg33HK8nIrwhYHC3ZmVtKcZWNyGALE/pFBTqcTmEYBtdMGgDAP7cebfX595yb0uj8ppu4T054Dfz+HiLH3gJAwYc/wFFb1uB8dwIudOB5mIIiOvRauquw4a6lPadKRjrqqqiqf9wc3k+VjSK9lNcLbNx27drFoUOHqK2tbXD80ksv7XBQIiIiIiLd2Te5ZRwqriLEYuK8lPZVpvUND+bqCf1Zvvkwz6w/wLQhfXwcZfdTUmXjy8PFAFwQwGRkZIiFmcPj+DCjgNW7j3mq6boK90zNs4fEYmnjQpee4OoJ/Vny3rf8L7OA7JJq+kWHtPjcvHLXv2ODzCYMA0bFR7B0VuopN3H3+c4jVB38kLrSAxR+9iv6nv+M5zFPi/aw3tei7RY25GLAoDZvC3XlR7FE9G/ynNJtz2CvyMYSOZgBN+7EsAR3fqAiEnBe/z/T/v37GT9+PGPHjmX27NnMnTuXuXPnMm/ePObNa33zlYiIiIhIT7e6viryvJS+hAW1+/f/3Dp9CABv7cgmt7TaF6F1a5/uP47d4WRE33AGxYYFNJZL6ite3RWwXclnnuU1vaNF221oXBhnDYnF6YQ3trdcHWmzO/jjun0APDl3DFUPz2br3TNOmYgEMAVF0nfW8wCuzdEHPwTAXnGMmpwvANfymt7KHJ5IcNIZAFTuf7fJ4/bqIkq+ehSAmGlLlYgU6cW8TkbecccdDB06lLy8PMLCwvjmm29Yt24dU6ZMYe3atX4IUURERESke3n3pHmRHTFpQAxnDYnFZnfy100HfRFat9YVWrTd3O336/Yfp7S69YUpncXpdLKufnlNb5kXebKrPVu1W05Gvr7tKIeLq0mMDObGKc0vpGlJ6MCZRI3/KQAFH/4Ie3UxlQfeA5wEJUzCEjmg3bH3BO7K0Mqs1U0eK/n6MRw1xVjjxhAx6prODk1EuhCvk5EbN25k2bJl9O3bF5PJhMlkYvr06fz+97/n9ttv90eMIiIiIiLdRmFlLRvqZzy6F510xOKzXYsz/rrxILV1jg5frzv72LO8JvDJyJS+4aTGh1PncPJhRn6gw/H4Nq+c/IpaQq0mpgyICXQ4ne7K8f0wmwy+PlLCnrzyJo87nU7+3yeuqsjbpw8lxGr2+h6x0x/CEpOCvfwIhet+TuW+/wK9u0XbLWzYbACqD32Mw3ZinmpdeTalW58GoM/ZD2CYvH/fRaTn8DoZabfbiYyMBKBv375kZ2cDrsU2e/bs8W10IiIiIiLdzAd78rE7nIxJjGRwn463Ei9ISyYpMpjcspo2bQnuqlak5zDhsbWE/Wo1Ex5b69VrWZGew5hHP2HXMVdyqaLW7q8wveKufF3dhVq13fMipw3uQ5Cl98yLdIuPCGZWajwArzWzyObd3XnszC0jItjs2VjvLZM1nPhZfwOgfNcrVO53JSMNa9MN3b2NNW4MlqihOO01VB38yHO8+Ivf4ayrIjh5GqFDZwcwQhHpCrz+f6exY8eyfft2AM4880weffRR1q9fz7Jlyxg2bJjPAxQRERER6S5WpOew+J0dAOSV+yZ5GGQx8aNpgwH40/qsDl8vEFak53D5y5tJzymjus5Bek4Zl7+8uU3vj/vcb0+qcrvp9W1dIjHrrnx979s8HA6n3+7jTSL3syz3vMjeu/DomkmuVu1/bj2K09nw6/L/1mYC8MOpg4kJtbb7HvbKY02OFX32SyoyV7b7mj2BYRhNWrVtRXsp2/kCAH2mP4RhGAGLT0S6Bq+TkUuWLMHhcLWHLFu2jKysLM455xzeffddnnrqKZ8HKCIiIiLSHbiTZsXVdQAUVNS2OeHWmh9OHYzFZLDhQBFbjhR3+HqdbdmaPRiAOy3kBIz6427NJdwyCyo8yd2TU0qGAQ+syeik6Ft2ztA4IoMtHCurYcvREr/cw5tErtPp9FRGzuhly2tOdtmYJMKsZjILKviqfvs6wKaDRazbX4jVbHDndzpWSFO06UFc38UnMyje9FCHrtsTuFu1K/evxumwU7Txt+C0EzrkYkL6Tw9wdCLSFXi92u+iiy7y/HdKSgrffvsthYWFxMbG6jccIiIiItJrNZtwq0+azW9lS29rkqNCmDo4ls+zCjnjyc8YmxTJ0lkjO3zdzpKRX0HjukEnsCOnjJtf30p0qJWnPsvyvH876hNuLXE6YU9+03mAnS3IYuLC1L6sSM9l1a5jTBkY4/N7ePN9tf94Jdml1VjNBmcOjvV5LN1FRLCFy8Ym8c+tR/nHlqOcMcj1Xjz6iasq8tpJA+gfHdqhe9QVZUAz39W2Io0uC+l/DqagaBxV+ZSlP09FxpuAQZ+zHwh0aCLSRfhkiEifPn2UiBQRERGRXq3ZhJuPkmYr0nP4vL791uHEqzbnrmBE35Zn6b28+QhPfeZqP2+u0TkiyNy0/syAkQkRvguwA9xzI90b1H2ptNrGN7nlbf6+cm/RPmNgDKHtWMzSk1xTv1X7ze3Z1NkdfJtXxr+/yQXgFzOHd/j6lthUmquMtMaO7PC1uzvDbMUaNxqA45+4ltwG9z+boPhxgQxLRLqQ3jfRWERERETED1LjmybcfJU0c1fHuZ1cHdcdNN4q7q5jWHphKr+YObxJSsct2GJi+dUTPa/Xfa7T6Tq3K7hklOu1bT5SQm5ptU+u6XQ6eX3rUU579BPszuZnUTaX4HUnI78zvPe2aLvNGhlPXJiVY2U1/C+zgD+s3Y/TCZeOSeS0xMgOXz926hJODByg/k8nMVOXdPja3V1F5kpqcjY2OFZz9PNeP09TRE5QMlJERERExAeunTygwee+TJr5s+qyMxwsdiXp+oYHEWIxMS4pindunML9F43kkTmjSUuObLb6cVRCBPPTknn7ximMS4pqcO68LtKinhQVwvA419b0QQ9+5PWmcGg4L/O0R/7HhMc/5Zp/bCGntIakyGDgRDLWzeZwUlJla3Bs3X5X9ex3evG8SDer2cQV4/sB8Md1+3n16yMA3HNuik+uH54yj4Q5bxDUNw3DHExQ3zQS5rxJeMpcn1y/O9M8TRFpjdczI0VEREREpKnjFa7EUHSIhZo6ByPjI1g6K9UnSbPU+HDSc8oaLnGh67Qqn0pFTR3/3ulqj1216AzP/L6TLZ01kstf3uxJ4DZO5M5PS+6y8zFXpOew73glAHUOp6eF/u0bp7QpZveCGvdcyD35FQBYTQZL6itH3/02jwfWZLAnv5wBMSFkl1TzbV455/9lI+/94EziI4I5XFxFVmElZpPBWYN77ybtk10zqT9/2XiQD/bkAxBmNZFbVuOz64enzCM8ZZ7PrtdTaJ6miLSmTZWRkyZNoqioCHBt0K6srPRrUCIiIiLiP81tLZaOcTqdvLMjG4C/Xj6eyodns/XuGT6r3ls6a2SDVmVw/VO/q7Qqn8p/dx2j0mZneFwYp7ew4KWrVz+eSkdb6BsvqHEb3jecX1+YSojVzPy0ZLbePYPKh2eT8avz+fzW6cSHB7HlaAkz/ryBoyVVnhbtSf2jiQxRzQnAsdKGiccqm6NbzVrtrjRPU0Ra06Zk5O7du6mocP2G7re//S3l5b5pB3n22WcZN24cUVFRREVFMW3aNN577z3P49XV1SxevJi4uDgiIiJYsGABx441HAx96NAhZs+eTVhYGAkJCfziF7+grq7OJ/GJiIiI9DTuKqz0nDKq6xzdbhFKV7Utu5R9xysJtZq4pNF8RF84OVlnNbv+kR9qNXHRyHif38vXXt92FICFE/qfcunlyQk3XyZy/a2jLfTNnQ9woLDlApAJ/aP5dPFZDIgO4du8ciY9vo6fvrMDgKzCSv0813vgo4YJ4e42a7W70jxNEWlNm35lNmHCBG6++WamT5+O0+nkD3/4AxERzbeELF26tM03HzBgAA8//DAjRozA6XTy8ssvc9lll7F161bGjBnDnXfeyerVq3nrrbeIjo7m1ltvZf78+axfvx4Au93O7NmzSUpKYsOGDeTk5HDDDTdgtVr53e9+1+Y4RERERHqLxlVYJ//jvKu2wXYHb9dXRV48KoGIYP9UpblblR0OJym//5gDRVWs3JnLtZMGtH5ygBRV1vLet3kAXF2/3binabaF3ovFRSPqzz9ZW84flRDJZ4vPZupTn3GsvNZz/HhFrVdt4j1ZRn3L+8m606zV7so9T7N400PYivZgjR1JzNQlmqcpIh5t+pvS8uXL+c1vfsOqVaswDIP33nsPi6XpqYZheJWM/N73vtfg84ceeohnn32WTZs2MWDAAF544QVee+01zjvvPABeeuklTjvtNDZt2sTUqVNZs2YNu3bt4qOPPiIxMZEJEybwwAMP8Mtf/pL777+foKCgZu9bU1NDTc2Jkv3S0tI2xywiIiLSnbVUxfWt/nHebk6nk7e3uyrRLh/Xz+/3M5kMbjx9IL9dk8Hyrw536WTkivRcbHYnacmRjEnq+Abjrsgz75KTkvxeLC6aNza5QTLSm8VHg/uE0ScsqEEyUr9gOKGjiWJpP83TFJFTaVOb9siRI3n99df56quvcDqdfPzxx2zdurXJx5YtW9odiN1u5/XXX6eiooJp06bx9ddfY7PZuOCCCzzPGTVqFIMGDWLjxo0AbNy4kbS0NBITEz3PueiiiygtLeWbb75p8V6///3viY6O9nwMHDiw3XGLiIiIdCep8eHNHjcbBtuzSzo5mp4hPaeMvQUVBFtMzD4tsfUTfODGKa6/v/4vs4CDp2jnDbSTW7R7Kk8LfXIUpvqu1BnD4trcZr4z11UYERdmbde8zKxmvv6q/nNpPGvVlxvuRUSk/dqUjDyZw+EgIcF3c3DS09OJiIggODiYH//4x6xcuZLRo0eTm5tLUFAQMTExDZ6fmJhIbq5rG19ubm6DRKT7cfdjLbn33nspKSnxfBw+fNhnr0dERESkK7v17KENPndP9Kq02ZnyxGf87N87efXrw1pw44W3TmrR7qzFIUP6hHFeSl+cTnh585FOuae3ckur+SSzAICrJvi/YjSQ3PMuN9w2HYCNB4vIa8PW5qMlVfz7G9dM/LU/Pbtd8zJT48ObrgpR9R/QvRcjiYj0ZO3629K+fft44okn2L17NwCjR4/mjjvuYPjw4V5fa+TIkWzbto2SkhLefvttbrzxRj799NP2hNVmwcHBBAcH+/UeIiIiIl1RQaWrnTM8yIzd4WRkfASLzx7Chxn5vLUjh6c+ywLwtJy6F9xo/lzzXC3armTkgnGd+/7cePoA/pdZwMubD7PkghGYTC0vhwmEN7dn43DCmYNiGBbXfEVuT3PGoFjOGBjDl4eLef6Lg9x3wakr8P72xSHsDifnDO3T7jZ2T5t4fdWfqv8acs9aFRGRrsPrysgPPviA0aNH8+WXXzJu3DjGjRvHF198wZgxY/jwww+9DiAoKIiUlBQmT57M73//e8aPH8+TTz5JUlIStbW1FBcXN3j+sWPHSEpKAiApKanJdm335+7niIiIiIiL0+lk+VeujpAn5471VGHdMnUwb9wwhfd/cCZB9Zuam1twI019k1vGnvwKgswmvje6c1q03RakJRMZbCGrsJLPso536r3b4vVtriTtVT10cU1Lbp3uqj7+y8aD2OyOFp9nszt4ftMhAH581pB230/VfyIi0t14nYz81a9+xZ133skXX3zB448/zuOPP84XX3zBz372M375y192OCCHw0FNTQ2TJ0/GarXy8ccfex7bs2cPhw4dYtq0aQBMmzaN9PR08vLyPM/58MMPiYqKYvTo0R2ORURERKQn2XiwiIz8CsKsZq5oZtHKrJEJGEbT6jrNn2vZ2ztcLewXjYwnKsTaqfcOC7JwZX37szvJ3FVkHa9k08EiTAZcOb5nt2g3dsX4ZBIigjhaUs2/drY8Ouq/u46RXVpNQkQQ89M6VkjhbhNvT5u3iIhIZ/M6Gbl7924WLVrU5Pj3v/99du3a5dW17r33XtatW8eBAwdIT0/n3nvvZe3atVx77bVER0ezaNEi7rrrLj755BO+/vprbr75ZqZNm8bUqVMBmDVrFqNHj+b6669n+/btfPDBByxZsoTFixerDVtERESkkZfqE1aXj0tucbbhSM2f88rb9fMiLx8fmOTPzae7Ftm8tT2Hsuq6gMTQHPfimpnD+5IcFRLgaDpXsMXMD6YOBuCZ9VktPu8vGw4A8P0zBhFsMXdGaCIiIl2C18nI+Ph4tm3b1uT4tm3bvF5sk5eXxw033MDIkSM5//zz+eqrr/jggw+48MILAfjjH//InDlzWLBgAd/5zndISkpixYoVnvPNZjOrVq3CbDYzbdo0rrvuOm644QaWLVvm7csSERER6dEqa+t4s75t9qb6BFZzPNtnTzqm+XPN25Vbxq5j5VjNBt8bHZgRQdMGxzIyPpxKm92zSMdfVqTntHmx0RueFu3eVRXp9uNpgzGbDNbtL2RHdmmTx/fml/PR3gIMA35Yn7gUERHpLbxeYPODH/yAH/7wh+zfv5+zzjoLgPXr1/PII49w1113eXWtF1544ZSPh4SE8Mwzz/DMM8+0+JzBgwfz7rvvenVfERERkd5mRXouZTV1DO0TxneGxbX4PPf8uQfWZLAjtxSnE+alJantsxnuFu1ZqfHEhHZui7abYRjcePpA/u/db1n+1WG+f8Ygv9xnRXqOa0kKrS822pVbxo6cUqxmgwW99Pumf3Qo88cm8daOHJ5en8XzV4xv8PhfNh4E4JJRCQzpExaIEEVERALG68rIX//61yxdupSnn36aGTNmMGPGDP70pz9x//33s2TJEn/EKCIiIiId5J4peOOUga1uXXbPn3v7hikArNt3nMrartMC3FW8k+7eoh3Y6r/rJw/AZMDnWYXs9dNszyXvfQu0bbHRP+tbtL87MoHYsCC/xNMduBfZvLblCIX1W+wBqmx2z8/jj6cNCURoIiIiAeV1MtIwDO68806OHDlCSUkJJSUlHDlyhDvuuKPZgeciIiIiElgHCiv5X6arJfTGKQPafN6lY5IYEhvK8Uob/9hy1I8Rdj978spJzynDYjK4bEznbtFurH90KLNS4wF4efORdl+ncRv229uzWbXrGJe+8CXf5jVNcjqdsDO3jMPFVScdc/L6Vtf3Sm/bot3Y9KF9GN8viiqbgxe/PLFg6M1t2RRV2RgcG8p3R3k35kpERKQn8DoZebLIyEgiIyN9FYuIiIiI+MHLm12JkPNS+jLYi5ZQs8ngtnNc1V1PfrYfp9PZyhm9h3txzQUj+naJ6r+bTne1Z7+y+TB2h/dfJ3cbdnpOGdV1DnbklHHl37/m0he/ZNXuYy2eZ3c6Gf67j/n+69t4+vP9jHz4f+w7Xulq5+7l3y+GYbD47CEA/Hl9lufr8peNBwDXrEhzK1XKIiIiPVGHkpEiIiIi0rU5HE5eqa+Wu3FKy4trWvL90wcREWxm1zHXwg1xcc+LvHx811jQcumYRMKDzBwpqSbs3tYXzDS2bM0ezzzIk5kNuPM7w3hy7ljA1Zp98p9jEyOoczhZvvkwd/zrGzKPV0L9da57batXMfRE10zsT2yolQNFVazefYytR0v44lAxVrPBIj/N9xQREenqlIwUERGRLsmbzb3SsnX7j5NVWElUiIX5ad5vfI4OtXqq7p5ct9/X4XVLe/PL2Z5disVkMHdsYLZoN/but3lU1NoBsNmdngUzbf25ycivaJKIBLCYTTx26Rhumz6Ut2+cwrikKEIsJsYlRfHOjVPY8Ytz2XDbdKKCm+7FbGmmZG8SFmRh0Zmun59n1mfx7IYDAFw+rh8JkcEBjExERCRwlIwUERGRLqdxy6i3iRU5wb0o48rx/QgLapowaovbzh6CYbgSXnuamR3Y27irIs9L6UufLtCiDScqG91OtWCmOanx4U2OGQaMSojwfO5ebFT58Gy23j3Ds2F96uBYau2OJuc7nbDHTwt1upOf1C+p+TCjgL99cQiA0Se9ryIiIr2NV8lIm83G+eefz969e/0Vj4iIiEiTllFvEyviUlZd50mc3Xy69y3abiPiI5hzmmtJy9OfZ/kktq6qtYrcFek5PPCh6/tw17GyLpMgb66y0Ztk4K8vTG3wuWG4zl/a6HhLUuPDaTz90DBgpJJubM0uaXLs1x/s6TLfOyIiIp3Nq2Sk1Wplx44d/opFREREBIBv88o7lFgRlze3Z1NpszMyPpypg2M7dK07zhkGuCotiyprfRFeQJwq2dhSRe4j/9vL+9/mcee/d3L5y5uprnNVAR4tqe4yFbsdTQYOjnUtNjKA4JPasN3Vj61ZOmuk55cG7nt7k8zsyRpXrYJ+uSIiIr2b123a1113HS+88II/YhEREZFe7kBhJTe8toVae9Ppdaqy8t7yr1wtoTedPgjD6NjW3nNT4khLjqTSZueFLw/7IrxO11Ky8dYVO1jy3rfc8sY2oGFFLsC9737LJX/7gic/a1gV2pUqdhsnA8G7ZOBrW48CcOWEflQ1asNui/lpyc3OlPTmGj1VR6tWRUREehqvBwfV1dXx4osv8tFHHzF58mTCwxvOl3n88cd9FpyIiIj0XCvSc1i2Zg8Z+RUMiwtjaJ8wPswoaDB7rkGrtqqs2mxFeg73vbubPfkVAMSFWTt8TcMwuOOcYdzy5nb+9HkWPztnKBZz9xo/3lz7P8CfNxxs9dwJ/aLYkVOKo1FWqaskldzJwAfWZJCe64pz5vC4NiUD7Q4nb27LBuDqif07FMN8JR+bSI0PJz2nrEFCUr9cERGR3szrv0Hu3LmTSZMmERkZSUZGBlu3bvV8bNu2zQ8hioiISE/TuEJt17FyVu/Oo9bu4IIRffnqZ+e4qqySo7CYXKVeKXFhqrJqA/d7605EAvzw7R0+aSW+ZmJ/+oYHcai4in/tzO3w9TpbSxujTQb8aNpgkiKDm22nHd8vii13zWBsUmSXnovoXjDz2eKzAdh0sKhNLfXr9h8nu7Sa2FAr3x2Z4O8wex21sIuIiDTkdWXkJ5984o84REREpBdpXKHmNqxPGGt+NA2AyQNimJ+WTF5ZDYMe/IjM45V8eaiIMwZ1bPZhT9fce+tuJe5o1VqI1cyPpg3moY/2ctPr27jhn1tJjQ9n6ayR3aIiLjU+nB05ZQ2OGQakJUfx7IJxXJgaz+Uvb/YkixonjZbOGnnKx7uKqYNjSUuOJD2njFe3HOW26UNP+Xx3i/aCcckEWbpXtWt3cHLV6p78ckbGR7B0Vqp+uSIiIr1Wu/+2kZmZyQcffEBVVRUATmdzv2cWERERaaqlCrXs0uomxxIig7lyvOsf7c+sP+DfwHoAf8+nGxwTCkClzd5g7mJXWOLSmoUTGrYgN04mtjb3sLvMRTQMgx9OHQzAcxsPnvLv6TV1dt6p37jekRZtOTV31WplO+ZxioiI9DReV0YeP36cK6+8kk8++QTDMNi7dy/Dhg1j0aJFxMbG8thjj/kjThEREelBBseGNmgjhlO3u946fSivbjnKG9uy+X9zRpMQGdwZYXZLI/qGk57btPrPV63Ef1rf8hKXrl4dubfA9T0XG2qlymZvtkKttbmH3WUu4rWTBnDPql18c6yMDQeKOHton2af98GefIqrbPSLCuE7w+I6OUoRERHpjbyujLzzzjuxWq0cOnSIsLAwz/GFCxfy/vvv+zQ4ERER6Xnq7I4mlXuttbueMSiWMwbGUGt38PwXrS8b6c3OapR08nUrcUajJDJ0nSUup1JabfMsafnP98/o8RVqMaFWTyXoc5ta/pn5Z32L9sIJ/TCbOrZxXURERKQtvE5GrlmzhkceeYQBAwY0OD5ixAgOHtQ/DkREROTU/t/afWTkVxBqNXFaQkSb210XTx8CwF82HsR20sZtOaG2zsH73+YB0C8q2C+txKnx4V16iUtLXttylEqbndMSIjhrSO+YO/qj+lbtN7dnU9jMIpuy6jr+841rEdE1k9SiLSIiIp3D6zbtioqKBhWRboWFhQQHq2VKREREWrY9u4T71+wB4M/zx3Hj6QPbfO6V4/vxi//u4mhJNf/emcvl4/v5K8xu65WvD3OwqIqkyGD23ns+oVazz+/hWeLCiSU5XXGJS2N/++IQALdMHYRh9I4KwDMGxTC+XxTbs0v5+9dHuOOcYQ0e//c3uVTZHIzoG86k/tEBilJERER6G68rI8855xxeeeUVz+eGYeBwOHj00Uc599xzfRqciIiI9Bw1dXZu/Oc2bHYnl41J5IYpA1o/6STBFjM/qK/0ajy3UMBmd/C7j/YC8Itzh/slEQknlrikJUd6KiTnjk3q0u3OW44Us+VoCUFmE9dP9u77rjtrbZGNu0X76on9e02CVkRERALP62Tko48+ynPPPcfFF19MbW0t99xzD2PHjmXdunU88sgj/ohRREREeoD712SwI6eUvuFB/PXy8e1Kfvxo6mDMJoN1+wvZkV3qhyi7r1e/PsKBoioSIoI87bn+Mj8tmW13z+RfN58OwMd7Cyhqpg24q3i+vipyfloSfcN7VyfPtZP6E2Y1szuvnM+zCj3H88trWJORD2iLtoiIiHQur5ORY8eOJSMjg+nTp3PZZZdRUVHB/Pnz2bp1K8OHD/dHjCIiItLNbThQyP/7JBOAv14+rt3bsAfEhDJ/bBKg6siT1dkd/O5jV1Xkz2emEBbk9SSedpl9WiJpyZGU1dTx9OcHOuWe3qqoqeO1La4KwFvO9G+StiuKCrFyVX2y8fmTFtm8vSMHu8PJ5AHRXX7ep4iIiPQsXicjAaKjo7nvvvt48803effdd3nwwQdJTu66rTkiIiISGCvScxj3h7Wc86f1OJwwY1hch9t5b50+FIB/bDnS7FKO3ugfW46y73gl8eFB/GRa5yXcTCaDe88bAcBTn++nvKau0+7dVm9uz6aspo7hcWHMHB4X6HACwl0p+9aOHI5XuH5m3C3aV01QVaSIiIh0rnYlI4uKivjDH/7AokWLWLRoEY899hiFhYWtnygiItINrUjPYcJjawn71WomPLaWFek5gQ6pW1iRnsPlL29mZ26ZZ9HJp/uPd/j9mz60D+OSo6iyOXjxy8MdD7Sbq7M7eKi+KvLumcMJD+6cqki3K8b3I6VvOIWVNv668WDrJ/iANz+T7hbtRWcOwmTqnXMRpwyMZmL/KGrqHLzy9WEOFVXyeVYhhgELJ2gRlIiIiHQur5OR69atY8iQITz11FMUFRVRVFTEU089xdChQ1m3bp0/YhQREQkYd0ItPaeM6joH6TllXP7yZiUk22DZmj00Tv0YBjywJqND1zUMg1unDwHg2Q0HsDucpz6hh/vntqNkFlQQF2blp2cN6fT7m00Gvzw3BYDHPt1Htc3u1/t58zO5M6eUTQeLsJgMbprS9s3tPU3jRTb/3JoNuCqVB8SEBjI0ERER6YW8TkYuXryYhQsXkpWVxYoVK1ixYgX79+/nqquuYvHixf6IUUREJGB++8EeAE9lnxPfJNR6g93HymmcJnQ6YU9+eYevfc3E/oQHmckqrCTs3t5bsWp3OHmofoP2XTOGE9HJVZFu108ewMCYEHLLalj+lX+rVZe+/y3Qtp9Jd1Xk90YnkhQV4te4urprJg4g2GJiT34F9767G0CzIkVERCQgvE5GZmZmcvfdd2M2mz3HzGYzd911F5mZmT4NTkREJJByS6vZmVvW5LivEmpdQWvtru1tUX92wwFszVQsGoZvEiDv78mnotZVgWezO7tdxaqvWv/f2HaUjPwK+oRZufXsoT6Osu2CLCZ+PtNVHfnoJ5nY7A6/3GdfQQW7jzX92XM6YXdew+PVNjuvfn0EgFvOHOSXeLqTD/fmU1PX8Ovy140Hu83PjIiIiPQcXicjJ02axO7du5sc3717N+PHj/dJUCIiIoH21aFiTn/ysyaVfeC7hFqgtdbu2t4W9Uf+l8niFemezw3jxJ9OJyy9MLXDsTduAe9OFau+aP1fkZ7D+MfWcv1rWwGYlRpPZEhgqiLdFp0xkPjwIA4UVXmWo/jSl4eKOOvpz5v9mQSotTv41erdVNQv0XknPYeiKhuDYkKZNTLB5/F0N/4amyAiIiLirTb9rXXHjh2e/7799tu54447yMzMZOrUqQBs2rSJZ555hocfftg/UYqIiHSiVzYf5kdv76CmzkG/qGCyS2swOKkt1EcJtUBzJydObncFuObVrxmbFEVGffVnc+2w85vZiO10OrnvvW95+H+uTol7z09hUv9oHvxwL3vyyxkZH8HSWakd3qYNkJFf4bcWcH9r7n0/1fvamDuZefI1Xt+WzeXj+7XpfH8JC7Jw54xh/N+7ru+B6yYN8NnCmH/vzOWaf3xNlc3B0D6hZBVWeZLbJ78Pj36SyT+3HmHhhP48u+EAADV1dv79TW5A35uuoDv/zIiIiEjPYjidzlanvptMJgzDoLWnGoaB3e7foeX+UFpaSnR0NCUlJURFRQU6HBER6WQr0nNYtmYPe/IriAwyU1BpA2DOaYn8/ZqJfJxZwANrMtiZW4rdCWcOimHj7ecEOOqOC/vVaqrrvG+nDTabqHpkdoNjDoeT2/+1kz/XJ4Aenn0a99QvNfGHCY+tJT2nrEFyxQDG9Yti610z/HZfX2jpfQ+2mKh6eHYzZzQ0/g9rG2woB1cyc1xSFFvvDuxrL622MfjBjyipruOtGyazYFzHNzX/6fMs7vj3TpxOuHhUAm9cP5k1Gfk8sCajQZLbYjK44187OVBU1eB8d7Ly7Run9OqEZLM/MwaMS+76PzMiIiLSPbQ1v9amZOTBgwfbfOPBgwe3+bldhZKRIiK9V3NVZgDz05J58/rJDSq7dh8rY8z/W4thwJ5fnkdK3/BOj9eXxtcnJ05mAMP7hvHHS8fy0xU7OFxc3ey5l41J5KdnD6W0ysayDzP45lgZ7hGRf56fxo/9vNXZ83Wrr45ze/uGycz3QQLMn5pLCgGYDXj0e6P56VlDCLaYm5xXbbPz4peHuXVlepPHAEIsJirbkMz0t1+//y0PfbSXEIsJJzAyPpyls0Z6lQhckZ7Db9fsYVduOfb6L/AtZw7iz/PTsJhbnjJUWVvHsN99TF55bYPjXSVZG0iNf2bcf75z4xSfVCuLiIiItDW/1qY27e6YYBQREWmLxi2z4ErI7SuoaNJielpiJJeMSuDdb/P447r9PDM/rTND9bmF4/uTnvOt53N3cuKR2aOZPTqRP9rHNkxecOJ9+vc3x/j3N8eavW5CZLDfY5+flszbN07hgTUZfJtXTq3dgRNOmajqKpbOGsnlL2/2fO5+X+1OuPs/u3jqsyy+NzqRT/cfZ29+BSl9w5k8IJo1GfnklNY0e82uNMd0eFwYgKf60z0Ts62Vie6kWWMXjYxv9esbFmShtLquyXG1Izf8mfH12AQRERERb7Rr0nl2djaff/45eXl5OBwN24xuv/12nwQmIiLSGXbnlTedo0bLiYu7Zgzn3W/zWP7VIZZdNJK48CC/x+gvx8pdia3YUCtVNnuT5ERLyYtRCRH8ecMBnt1wgMYLs72ZfdhR89OSPfe5993dPPK/TH65ahcXj0rA2oWTkvPTkjljYAxfHi7GYjIYkxjJfReMoKS6jvvX7OFgURV/Wn/A8/yduWWere4DY0KYlRrPC18eblLh1lXmmD6xbn+Dz72diblszZ4mxwwDHvxwb5vavlPjw5ttR+4qydpAOvlnRkRERCRQvE5GLl++nB/96EcEBQURFxeHYZyoGjEMQ8lIERHpFpxOJ39ctx+bvem0klMlLs5NiWNCvyi2ZZfyl40HuO+CrpEA8pbD4eTtHdkAvHz1ROaMTmz2eS0lL56el8bfvjhETaPZh4GqQPvVuSm88MUh9uRX8LcvDvETP7eJd0RtnYPdea73aP2t0zl9UIznsasn9iPl9/8jt6xpBeSA6BD2/up8giwmLj4tsctWuGXkVzQ55s33xbd5HTvfXXnaVZO1IiIiIr2d12UDv/71r1m6dCklJSUcOHCArKwsz8f+/ftbv4CIiEiA1dY5uOWt7fz8v7s8x9y/W2stcWEYBnfPHA7An9YfoKau+y1uA1h/oJCc0hqiQyxcmNq3XdcYGR9O413JgapAiw61er5m93+wh9JqW6fH0FYbDhRSVlNHfHgQkwdEN3gsLMhCcVXzsRdU1BJkcf3VbX5aMlvvnkHlw7PZeveMLpOIBFdlYke+L0KtTf966s357orecUlRhFhMjEuK0lxEERERkS7E62RkZWUlV111FSZT121/EhERaUlBRQ2zntvIS18exmTA45eO4a0bJnuVuLhyfD8GRIdwrKyGf2w52onR+86b211VkXPHJjW7LKUtls4a6WnBhcBXoP1o2mBG9A0nv6KWRz/ZF5AY2uK9b/MA1wzExnNJoePJvEBr/H0Bbf++WJ9VSEn9zEf36e35vurKyVoRERGR3s7rjOKiRYt46623/BGLiIiIX6xIz2HCY2sJ+eVqBiz7kHX7C4kKsfDf75/Bz74zjAXj+nmVuLCaTdw2fSgAf/x0P05n01bvrszucPLOjhwArhjf/s3TXa0CzWo28cic0wB4/NN9HCmuCkgcrXl/jysZ+d1RCc0+3tWSvN46+fvCWp9sDTIbTBkYc8rznE4nv1jlqlY+f0RfxiV3je8rEREREfEtw+nlv6Dsdjtz5syhqqqKtLQ0rFZrg8cff/xxnwbYGdq6elxERLof92bexhuz/3jpGO74zrB2X7e4ysagBz+kvMbOu7ec2WJiqStat+84M5/dQEyoldzfzPK0/vYETqeTGX/ewOdZhdw0ZSAvXjUh0CE1cKS4ikEPfoRhwLH7Z9E3vPnN4yvSc7rsTEhvOJ1OZv55A59lFXLNxP68eu2kFp/71vZs/n97dx4edXX+ffw92RcyCYGssu+LCagoRBRkKYvYnwq21aKipbgUrBZEoVJQrKJYrbVVfOoGLlgrglZUZBeViIAEQoCEfU1CSMhO1vk+f4SMSWYCWWYmE/J5XVcurny3OWc4juTOfe77N+9tJ9DHk5RZw4ky+7lwpCIiIiLSWHWNr9X7p48FCxbw9ddfk56eTmJiIjt27LB+JSQkNGbMIiIiDjd/dbJNINJkgsVbjzfquSH+3ky+pgNQkYXXnFTdon0pBSKhoqbnCzf1AWDJ9uPsPJXTxCOqrjIr8pr2IbUGIuHS2WZsMpl46ea+mEywdMdJ4o9k2b2uuKyc2V/uBeDRG7oqECkiIiJyCat3N+0XX3yRt99+m3vuuccJwxEREXGslIwCam4BcFTH54ev78K/vj/C2v1nSDiZQ//Lgi9+UxMrtxh8knh+i3Zs8wxwXczAjq35Tf9oPko4xfX/+p5Si0HPsEDmjupptzO4K63ad+Et2peiq9qFMGlAexZvPc70/yXx/bTrbGplLtp8lEOZhUQG+TJjaNcmGqmIiIiIuEK90yF8fX0ZPHiwM8YiIiLicN3bBtocc1QzkE6hAdx2PqD3902HGv08V/j2UCbpecW09vdmRPewph6O0wzt0gaA/JJyisssJKbmcduSbSw/H4htCqXlFtbuPwPA2BYUjAR4ZmwvWvl6suVYNkt3VG/6dLawhKfXpAAwf0xPWvnW+3flIiIiItKM1DsY+fDDD/PPf/7TGWMRERFxuNE1gj6ObgYyfUhFFtd720/g//gX9H9xY5MGvC7GukU75tLbol3V6/FHqn1f2RDm6dUpTTIegM1HssgtKqNtoA8D2oU02TiaQpTZj9nDuwMw+8u9FBSXWc89s24/Z8+VcnlkEPde3aGphigiIiIiLlLvXz3/+OOPrF+/npUrV9K3b1+bBjbLly932OBEREQaa9vxbADCW/mQW1Tm8GYgx3N+7thcXP5zBt6ySQOafEtwTWXlFmug9NeN6KLdHKRkFNgcc9T2/Ib6al8GAKN7htlsU24J/jSkC//+4ShHz57jhY0HeXJ0Tw5nFvKv744A8Py43ni2wPdFREREpKWpdzAyJCSE8ePHO2MsIiIiDrU7NZeNBzPx9DCx7ZEhtAvxd/hr1GyQUzUDz92CkZsOZXE6v4TQAG+Gd2vb1MNxqh5hgSSm5tk0LnLE9vxKyxNTmb86mZSMAnrUoSZlS6wXWZWftycLb+rDb97bzgsbDzB5YAee+GovJeUWRnZv22LfFxEREZGWpt7ByHfeeccZ4xAREXG4f31/BIBbL490SiASnNsgx9Eqt2jfGhOFt+elu0UbYO6onty2ZJt1Wz44dnv+8sTUiudTEYC+WEbsyZxz7ErNxWSqyIxsqW6LjeL6zqF8eziL3s+vp7DUAsDonuGYTMqKFBEREWkJmvQnkQULFnD11VcTFBREeHg4t9xyC8nJydWuKSoqYurUqbRp04ZWrVoxYcIE0tPTq11z7Ngxxo0bR0BAAOHh4cycOZOysjJERKTlOltYwvvbTwAwdXAnp71Oj7BA7IVQeoTZNs5pSmXlFla0kC3aAONjolg2aQCxkWbr1t9ubQK45fLIOj9jeWIq/V/cSMCsn2uBGobBwTMFTP8sCbCfEWvPqvNbtK9uF0LbQN8Gz6u5M5lM3NQ3AsAaiASYuXKPW9daFRERERHHqXdmZOfOnS/4m+tDh+reTfSbb75h6tSpXH311ZSVlfHnP/+ZUaNGsWfPHgIDK36I+9Of/sQXX3zBxx9/THBwMNOmTWP8+PF8//33AJSXlzNu3DgiIyPZvHkzqamp3H333Xh7e/Pss8/Wd3oiIk3mYls+67sltKVbvO04haXlxEQFMeR8Z2VnsJeBB9DeSZmYDfXNoUwyCkpoE+DNsK7Oez/cyfiYKMbHRHE6r5hOz6zlQGYh6/afYWSPi2cm1sx83HU+8zHQx5OCknK791woI7alb9Gu6oPzvySoyl1LG4iIiIiI49U7GPnII49U+760tJQdO3awatUqZs6cWa9nrVq1qtr3ixcvJjw8nO3btzNkyBBycnJ46623WLp0KcOHDwcqton37t2bH374gUGDBrF69Wr27NnD2rVriYiIoH///jz99NM8/vjjPPnkk/j4+Ni8bnFxMcXFxdbvc3Nz6zVuERFHq23L5+sTYhjTK5xV+07zwCeJdd4S2tJZLAavnd+iPW3whX+J1liVGXhPr04hOSOfiCBfjp49xxd7T/Pe9uPcdVV7p712fVTdou11iW/Rrik8yJffD+rIv747zIJ1++sUjKxZC7RSQUk5Pp4eeHrAuSqZfZV6htnWpCwtt7Bmf0Vm5FgFI92yuZCIiIiIuE69g5EPP/yw3eOvvvoq27Zta9RgcnJyAAgNDQVg+/btlJaWMnLkSOs1vXr1okOHDsTHxzNo0CDi4+OJiYkhIiLCes3o0aN58MEHSUpK4oorrrB5nQULFvDUU081aqwiIo5krwkKwAOfJFa7rjk0SXEHX+07zcHMQkL8vfntFZc5/fUqM/Aq/WXVPp5Zu5/7P95F34ggrmwX4rTXrktG7VNfJ5OYlgdAtNnPaWNxZzNv6Mrrm4+w4WAm8UeyiOsUesHrk+3UAgXw8TSR+8xYVu5Nt5sR26VtgM098UfOkltURpsAbwa0D2ncRC4BrmguJCIiIiLuy2GpEWPHjuWTTz5p8P0Wi4VHHnmEwYMHc/nllwOQlpaGj48PISEh1a6NiIggLS3Nek3VQGTl+cpz9syePZucnBzr1/Hjxxs8bhERR6gt8AHg62X/o/pSyiSyV5uvMV79/jAAv7umPYG+9f69W6M9NaonN/YKp6jMwvjF28jIL774TbWo7b2xWAze+OEoty3ZRmJqHkVlFutW4tvf28781Snc/t72ivPnA5EA89ektMjafO1D/LlrQDsAnl23/6LXtwnwtjlmMkHviCB8vDyq1aT08/KgXXBFkHdFYhoLNxyodt9XyT9v0a6sX9mSzR3V0/oLFc7/6cjmQiIiIiLi3hz2E9qyZcusGY0NMXXqVHbv3s13333nqCHVytfXF1/flls8XkTcT3grH45nF1U7ZjJBbJSZHdOH0v/FjTaZRACdQt2rLmFD1Lcr8cWkZOSzKjkDkwn+cG0nRw+3Tjw8TLw/8UoG/uNb9p8pYMSizZhMJvafqV+9z9rqFl5m9uNMYQnFZRXbhGuui8ot2fa05IzaWcO6sWTrcb7Ye5qEkzn0vyzY7nX7TueRkV8CYH3v7QXMambEvrDhAI9/sZdZX+wlNMCb3w/sCKheZE01Sxv0DGvF3FE9uLUFrkkRERGRlqjewcgrrriiWu0twzBIS0sjIyOD1157rUGDmDZtGitXrmTTpk20a9fOejwyMpKSkhKys7OrZUemp6cTGRlpvebHH3+s9rzKbtuV14iIuLOTOefILCwFag981NYkJT2vhANnCujW1r06N9eHvS3qjQmYvXq+VuS4XhF0adN070uIvzcr7rmaAS9vYnf6zxms9Qm21la38GRukb3LrTxN8PtBHXnzh6OU17j5Usqora/uYa34db9o/pNwiufWH+A/d11lc01ZuYV7Pkyg1GLQL8qMgUFKRkGdAmYzh3Ujs7CUhRsO8MCyXYT4eXNtp1B2nsrFZILRPS9eq7KlqBnIFREREZGWo97ByFtuuaXa9x4eHoSFhXHDDTfQq1evej3LMAweeughVqxYwcaNG+ncuXO181dddRXe3t6sW7eOCRMmAJCcnMyxY8eIi4sDIC4ujmeeeYbTp08THl6RcbBmzRrMZjN9+vSp7/RERFzKMAx+/9+dFJaU061NAAE+nnYDHzUzibq0CSC3qIwTOUUMX7SZDQ9eS9dmGpC0t0W9oQGzvKIyFm+tKL0x7bpOjR9cI/WJDLI2tKlUn2BrSi3b9709Tex7bDi3vPMju9Nsa+9dHmVm0YRY4o9kqTZfDbOGd+c/Caf4eNcpnjrd0+a9eG7DAX48nk2IvzefT76GdvXsir7gxl5kFZbw5pZj3PHBdsznywT4eXmw6VCWAnAiIiIi0uLVOxg5b948h7341KlTWbp0KZ999hlBQUHWGo/BwcH4+/sTHBzM5MmTmT59OqGhoZjNZh566CHi4uIYNGgQAKNGjaJPnz7cddddLFy4kLS0NObMmcPUqVO1FVtE3N7/iz/K18kZ+Hl58L/J19ArPKjWa2tmEqXnFTN80Wb2ns5n+OsVAcmmzARsiILiMrw9TNirqBjs543FYuBRjxp7720/QV5xGT3CAhnZ3T2y0NLzbGdX12Br59AA9p6ufp3JBH0igujcJoB5o6tnzF4so1a1+SA22swv+0Tw+Z50nl9/gLdv728999OJbOavTgHgn7deXu9AJIDJZGLRhFiS0nKJP5rN2XNlQEXn7caUHxARERERuVQ4rIFNQyxatIicnBxuuOEGoqKirF8fffSR9Zq///3v3HTTTUyYMIEhQ4YQGRnJ8uXLrec9PT1ZuXIlnp6exMXFceedd3L33Xczf/78ppiSiEidHThTwKOf7wFgwbjeFwxE2hMR5Mu6B+LoFd6K49lFDPrHd/RZuMFhTWCcLftcKaPf+IH8knKgYot6Ven5xdz27jbyisrq9DzDMKyNa6YO7lyvIKYz9QgLtJlbXbITDcPA37v6/6ZrBhNrNlGJjTTzyaQBNhm1tZ1vqWaP6A7A+z+d4GhWIQBFpeVM+jCBMovBhJioRnVh9/QwkV9cbnO8MiNWRERERKQlMxmGYW8HmA0PD49qtSLtPsxkoqysbj80upPc3FyCg4PJycnBbDY39XBEpAUotxjc8Nr3fH/kLMO6tmHN/XENDp6l5hYx4OVNpOb+nIFXWWfQXbOw0vOKGfvGDyScyiXE35uZN3TlvwmnrM0sruscyhtbjlFSbqFPRCs+vfeaC9bFXJ6YyszP93A4qxAPEyy+vT93XtXehTOqnbUJTY16n8vuvorxsdG13vf+9hPc/eEOvDxMdG0TwNGz59Tow4FGvh7P+gNn+MO1nfjX+BgeW7mHv208SHgrHxIfvYGwVo3bXREw6wuKzjcYqsrPy4PC58Y16tkiIiIiIu6orvG1Om/TXrFiRa3n4uPjeeWVV7BYbP/RLSIitl785iDfHzlLkK8Xb/+mf6Oy+KLMfoT4eVcLRja2CYwzLE9MZf7qZJJPF2BgUFJuEBHky9dTBhEbbbZmq1WaeFU7JizZyp70fPq/9A0RrXxIzS2u1o26rNzCG1uOMnX5but9FgPu/jCBAB8vt5h71Xqfe0/nUVpuYIDN9uuq0vOKeeSzijnNG9WDJ0a23G3VzvLnEd1Zf+AMizYf4Y0fjlJqqYgU//tX/RodiISKjFjV6xQRERERsVXnYOTNN99scyw5OZlZs2bx+eefM3HiRG2NFpFmrTJYlpJRUC3g5ejn7zudT8n5Fscv39yXjqEBjX724fNbTatyp67J1uxAqneGnjOiO7HR9n9jNqhja7Y9MoRhizaTklHA4ayKJjC7znejjgzy5UxBCWUW2wR/dwvEVq33+faPx/j9f3cy9+tkrm4fwqie4TbX/3FFIlmFpfSPNvPYsG6uHm6LcPZcCVCxHkurrCF766khVK9TRERERMS+BtWMPHXqFFOmTCEmJoaysjISEhJYsmQJHTt2dPT4RERcojJYlpiaR1GZhcTzAS9H1V2s+vzKQCSA2a/efcTssluXEPfJwpq/OtkmEGkC3txy7IL3RZn98PG0/7+qtLziWgNH7hSIrel313Tg9wM7YBjw2w9+4kiNQPKKxFQ+3pWKp4eJt37TH+9a5i+N8/SaFLu1PB1V01H1OkVERERE7KvXTzg5OTk8/vjjdOvWjaSkJNatW8fnn3/O5Zdf7qzxiYi4RM1gWdVtzs54PlQE4/66Zr9Dnj93VE/rmCsZwGM3dHXI8xvjUGYBu9Oqb1eFivHVJWB44EyB3ePeniaOzRlJTFRQgxrENKVXbrmcAe2CySos5VfvbqOotKLZSVZhCVOXJwIw84auXHFZcFMO85KWklFguyYdHMQeHxPFjhlDKXxuHDtmDFUgUkRERESEegQjFy5cSJcuXVi5ciUffvghmzdv5vrrr3fm2EREXMZZgYnisnI+3HGSxEYE4+qiahaWr5cH3udrUG49keOQ5zdEUWk581en0PeFjdhLYKxrwLC2btR9IoJoF+LPvBqB2OawHdbP25OP7x5AmwBvtp/IYdqKivqQM/6XRFpeMb3CW7n1+C8FDe1yLiIiIiIijVOvbtr+/v6MHDkST0/PWq9bvny5wwbnKuqmLSL9X9xo02wCoGdYIHsfH16nZ1StOdmxtT89w1qx+ehZzhSU2L3eZILYKDM7pg9t5Ohtrdp3mhvf3ILJBN9Pu45BHVs7/DVqqjr/KLMvhSXlpOdXzD02KohdqXk29fPqsm21Zjdqe/cuT0zl6dUp1m7czaXj9JqUDMa+8QMWA1r7e3P2XCkAz4ztZdPQRxyrLutKRERERETqrq7xtToHI++55x5Mppo5BLbeeeeduo/STSgYKfKzizVxcXaTl6byyc5T/Oq97TbHQ/y8iP/j9RfNlqqtQQtAu2A/ru3Umv/uTHVp4OOeD3fw7vYT9A5vxU/Th+DrVfsvkhqrtvm39vfmtQkx/LpfNCt2pzU4YNhcg411cecHP7F0x0mb48smDbgk/ttyZ5fyuhIRERERcTWHByMvZQpGilSoLaA0aUA7YqPN7DqVy5JtJ6zHK6+7FIImKxJTmbBkGx4m8Pb0oFubAPJLyjl69hxRZl82PHgtPcJqD0j2XbiBvadtt1x3CvUn5fHheHl6uDzwkVVYQt8XNpKeV8yfR3Tnr2N7OeV1zpWW02fhBo6ePWdz7vLIIHY9eoNTXvdS0e98Vm5VJhPERprZMcPxWbMiIiIiIiLOUNf4mmPauIrIJcFekxWgWgCyKoOKgOTTq1OadTDSMAyeXVfRSObx4d14ZmxvADLyixnxejy70/IYviieDQ/G0b1GQNJiMXhjy1G7gUiAtNxivM53Qx4fE+XS9yk0wIdXx8dw25JtPL/hABNioxrVEKVqVmy3toHc2DucE9lF/G9PGvnF5Xbvqa35jPxsf4bte+TO3cBFREREREQaQ8FIEbGy18QFwMMEt/e/jP8knLRpRGIAe07n2bmr+VidksH2EzkEeHvyyPVdrMfDWvmy9v44RrweT1J6HnH//I6wQB+Onj1Hj7BA7rm6Pct2pbL5yFm7z3WHZhjjY6K4LTaKZbtSmfxRAlsevh5vzzr3LrOqmTW7Oy2P3Wk//717e5gorbE43GH+zUGPsECbeqV670RERERE5FJV/59IReSS1aG1v80xkwliosy8P/FKLo8Msuk+C1BabvDXtSlY7LVMbgYWnM+KnDKoA2GtfKudCw/yZd0DcbQL9iOrsJTkjAKKyizsSs1j+v/2sPnIWVr5enLv1e0B9+zo/M9bYwgN8CbhVC4d/7qWgFlf0P/FjSxPTK3zM+avTgZss2bbBvqw+aHrWHrnlYB7zt/dzW2G3cBFREREREQaSsFIEQGgrNxCuZ3MtqpBEXtBk0pzVyXz6/e2kV9c5qIRO8Z3hzPZdCgLb08TM4Z2tXtNeJAvQX72E8nNfl7smTmMt37Tn2WTBhAbacbPy4PYSLPbdOWNCPJl4pWXAZCWV0xRmYXE1DxuW7KtzgHJfbVsQ88vLmNQx9ZMiI122/m7u/ExUXrvRERERESkxdA2bREBYMH6AxzMLCTAx5NOrf05lFlo02SlMmhSswlLZmEJU5cnsjwxjZSM73ggriP//uFos+i4/ezaiqzISQPa0y7ENjO00uHMQrvHS8os1vtcXROyPr45mFnt+8qgcl3qfeYWldo9XnMrsTvP393pvRMRERERkZZCwUgR4acT2Ty9JgWAf98Wy2+vbFfrtbUFTfpGBDFhyTZ2p+UxbcVua23Bygw8d+y4/dOJbFYlZ+BhgseHdbvgtc29rl9KI5qkPLRiNyXlFTOvzJbVVmIRERERERFpCG3TFmnhikrLmfRhAmUWg9tio7jjissa9Jy4TqFsfeR6/L0rPlYqg3ZVM/DczYJ1B4CK5jxd2wZe8NrmXtevR1ig3XqfPcIuPO8PfjrBe9tP4GGCv47ppa3EIiIiIiIi0ijKjBRp4f6yKpmk9Dwignx5bUIMJpO9kFXdXBbsb9NtG+qegedKe9PzWL67ol7irOEXzoqE2reoN5dg3NxRPSu6YZ8PolYKDfDBYjHw8LD9ez+UWcAfPkkE4C+/6MGfR3bnzyO7u2rIIiIiIiIicglSMFKkBfv2UCYvbToIVGzPbhvoe5E7Lq6nm29nXp6YyvzVyexOy8Mw4Jr2IVweZa7Tvc25rl/NYGpkkC/Hss+x8WAmMz5P4qX/61stEF1WbuHOpTvIKy5jcKfWPDFCQUgRERERERFpPAUjxW1VBo1qa4JysfNSu+WJqTz5dTJJaRVBw2Fd2/DLvpEOebY1A48qW7XdZDvz8sRUm7H9eDyb5YmpLWLt1AymvrvtOPf8J4F/fHuYNoE+zBn589/R/DUp/HD0LMF+Xrz/2yvx8lRVDxEREREREWk8/XQpbqkyaJSYmkdRmcXaBGV5YmqdzkvtKt+73Wk/Zy9uOJjpsPeuMgMvJiqIyp2/13Zs7RbbmeevTq4WiAT3rWfpCncPaM/fb+4LwNxVybz6/WEANh3M5Nl1FV3GF02IpWNoQJONUURERERERC4tJsMw7FR4a1lyc3MJDg4mJycHs7lu2zXFufq/uNFmqy9AWKAPdw1ox7vbTnCmoKTaOZMJYiPN7Jgx1HUDbYbsvbfOeu92ncrlyr9/g8WADQ/GMbRrW4c+3x57GbP9o818vDOVP3+512ZNAfh5eVD43Dinj81dzfs62dpNPdrsx6ncIqAiY3bdg9c25dBERERERESkmahrfE2ZkeKWkjMK7AaNMgpKeOmbQzaBSHDPJinuyN5766z3LjbazJRBHQH402dJlNvrbuNANTNmd53PmO22YD2zawlEulM9y6by5KgejOkVBmANRIJjM2ZFREREREREQMFIcUN5RWV42+nsCxWZkTNv6EpYoI/d813bXBrbSZcnptL/xY0EzPqC/i9udGhAKNjPtlSsMwNy80f3JNjPi4RTubyz9ZhTXqPSnK/2AdgNOo7s3pb74yoCo5V9Wio7S7tDPcumZDKZOJlTZOd4y93CLiIiIiIiIs6hBjbiVk7nFXPTW1vILykHsNb3qwwavX5bLLfGRDGwY+uKRiTnj1dKzyvhpxPZXNkupCmG7xA1m6xU1sNcNmlAo5us7DudR2ZhRVZpzffWWQG5sFa+zB3Vgxn/28Ocr/bxq9hogv29G/y8mtuwpw3uTH5JOR/vPMW+0/azO329PFh9fxwAv+gRZu0o3TOsFXNH9XCLepZNbX9Ggc0xZRuLiIiIiIiIo6lmJKoZ6S4OZRYw5o0tHDhTQNtAH2YM7cJ/dpyqNWi0PDHVGlTq2Nqf3KIyUvOK8ff24M1f9+eOKy5rwtk0nLNqOhqGwfBF8XxzKJMrLzNTZjFIyShwSUCupMxC7IsbSckoYMbQrrzwyz4Neo69btgXYzJBbJSZHdNVS/RCal13eu9ERERERESkDuoaX1MwEgUj3UHCyRzGvrmF9LxiOrb25+v7BtEjrH7bhrPPlTLxg5/4at9pAP6vbwSHswrZX6WRSWMzC13Bf9YXFJdZbI57e5jIfmYs/t6eDXruu9uOc89/EvD39iBp5jA6ubhD8pd707nprR/x9jSx+9Eb6F7Pv1+ovbFRoI8nz97YG38vD+5btsua7Vn55yeTBij78SKsgV69dyIiIiIiItIACkbWg4KRTaNyu+3e0/mUWwwsRkUW1pe/H0h0sF+DnlluMZizah/Prz9Q7XhlJp0jtjo7k8Vi0HbuKrKLyuyeD/H35u4B7egSGsDbPx6r1jH6QvPKLCih98INnCkoYcGNvXl8eDdnTeGCbnzjB1YlZ/B/fSP49N5r6n1/wKwvKLITqK3aDbtqxqy2YdeP3jsRERERERFpKAUj60HBSPtq1uZzZGZhbdtt3729P3cOaN/o53f66xqOZVdvyOGIrc7O9ujnSbz0zSGgSk3H83+GBfqQYaeLeF0CrVM+3slbW47RNyKIn6YPwduzaXpX7U3PI+ZvG7EY4O1pond4q3qtq74LN7C3Rl1IbSUWERERERERaXp1ja+pm7bYVRksTEzNo6jMYm2i4qiuzvNXJ9sEIk0mePF8IK6xTufbBu3cvRnH3zcdtAYiH7quE7FRZvy8PIiNMvPJpAGkzhvFl78fiNm3et+pyiY0tXU9/v5wFm9tqehivei2mCYLRALsPZ2P5fxfemm5Ue91VTNjVt2wRURERERERJoXddMWu2oGCysz9GZ+vochXUJpG+jbqMzJPen5NnX/HBks7BEWaLe2YM8G1Cl0hf/sOMmM/+0BuOA26jG9wikpt92mbBiw93SezfHScgsPfrILgN9d04HrOrdx4Kjrz+66Oh9IvdjaSUrLY8OBMwB0axPAiZwibSUWERERERERaWYUjBS7UjIKbIOFwOGsQsLnrbbZMlyZ4XaxmoyGYTB/TQplFtvqACYT9Ax3TLBw7qie1ZpxVOoX7T7b8KvWzCwtrxjktOs689iwrhe8r7ZAa0m5wewv9/LUqJ74eFVkP/590yF2p+XRNtCH58f1dsY06sXuuqpjEPrxlXuwGDAhJoqPJw1wzgBFRERERERExKm0TVvsau3vbfe4z/ktvjVrF1ZmTta2VRgqApGPfr6Hp6pcYzL9/Kcjt9uOj4li2aQBxEZWbHWONvsC8N5PJ/h0t2O2mjdG1W3wlYFIgCGdQzFVvim1mDuqpzWjkCp/Ajy//gADX/mWlzcdpM/CDcz6Yi8Av+kfTZtAH0dPo956hAVib3adQv0veN/alAy+3HcaLw8Tz97YyzmDExERERERERGnUzBSbCzafITUvGIAa+CoMuD14Z1XkvX0GLw9bUNKBpCYlsuhzAKbc+UWg/uW7eLvmypqIr58c99qwcLYyIq6iI7cbjs+JoodM4ZS+Nw4jv/lF9wf1xHDgDs/2MGOkzkOe52GqK1m5jNr91/03pqB1sr37pNJA2gb6MPOU7lM/98e9lVp9PLq90ccVu+zMWoGUisVlpZTUGy/g7jFYvDYyoot7A9e24nubrrVXkREREREREQuTt20UTftqpZsPc69HyUAcOvlkRzMLCAlo8CmNl//Fzfa3SoMFV2Spw3uTL9oMy99c5DkjAL8vDzIKSrDwwRv/Kof917TwXWTOq+03MJNb21hTcoZLgv244c/XsdlwRfOyHMW38dWUmpnq7qflweFz41r8HPTcovo+fx68orLqx13p07iyxNTeXp1CskZ+XRpE8CpnCKyi8r47RWX8d5vr7DJDH1323Hu+U8CZj8vDsweTttA3yYauYiIiIiIiIjUpq7xNdWMFKuPEk4y+b8JADx0XWdevrlvrVuGa9ZkrPyzX5SZnam51gzISsVlFU1X/jSkS5MEIgG8PT346K4BDP7nd+w9nc+QVzcT6OPJgTP1b8DTUPnFZUxbnmg3EOmImpmRZj9Kym2f7U6dxMfHRFV7n789lMnw1+NZuuMkgzq2Ztp1na3nzpWWM+erfQD8eUR3BSJFREREREREmjlt027hliem0v/Fjfg+vpI73v8JiwG/H9jhgoFIqH2r8I4ZQ/lqykB8vWyXlglYm3LGibO5uBB/bz6ffA1Bvl4czipkd1oeRWUWawOe+mxlrnzvAmZ9Qf8XN1703l2ncrn65U28u/2EzfZ3R9bM7GmnLqMjmwM52vVd2vDCTX0AmP6/JL4/nGU99/KmQ5zIKaJDiD9/rBKkFBEREREREZHmSZmRLVhlE5WatQtH9Qi7aBMVsM1wqzS6Z7jd6w3cIzuvS5tAwlv5kFelRmFlHcOnV6fUKTuy5ntnr5t4Zbfs5IwC2gb4kJ5fTJnFINrsx9KJV3KmsMS6XbnmNvjGqC1r1VHNgZzh4es7s+XYWT5KOMWv39vG9keG4GEy8dz6AwA8c2Mv/Lw9m3iUIiIiIiIiItJYCka2UAXFZTzy6W7AfhOV2/pFN+r5PcMCbWpKulN23smcIptj9dnK/OTXyRX3VN57/s8Hlu2iqLScrMJS/vjpbmuw8mRuxetdeZmZVfcNsm43dsa28MqsVWcEOp3FZDLxxq/6kZiay570fLo/t56i0nLKDegcGsAd/S9r6iGKiIiIiIiIiAMoGHmJq8zOS8moqIs4aUB7DmQW8sFPJ8gtsu1e7Kjagu6endfDTrAUoGcdOjUXFJexJz3P7rkzBSXcuXSH9ftqwVigzGK4pO5hbVmr7qyVrxf3x3Xi4U93U1DycwOew1mFfJqU1uzmIyIiIiIiIiK2VDPyEla5lTgxtaIu4q7UPGZ8vodFm4+QW1SGj6ftVmxHZS/WVlPSXbLz5o7qad2aXVWP8MAL3pdXVMa4t7Zgp/8MJqBNgDdxHVvbvdcAUjIKGjTeluKtLUft1rt8enVKk4xHRERERERERBxLmZGXsPmrk23qQQIE+3nx8d0DyDlXyq/e2+607EV3zs6ruZU5NMCbU7nFfLwzlT4Rycwb1dPmnpxzpdz45hbij57F39uDc6UWm/fu37/qx60xUfT720Z2p7nvNnV3lZJRYLNe3akTuIiIiIiIiIg0joKRlzB7gR2A4jILI3uEAbDMw9Ssags6Us1g6d82HuSxlXt4anUKnh4m5oz8OSh7trCEMW9sYevxbFr7e/P1fYM4ln2u1vdu3mj33qburuxtn1cQV0REREREROTSYTIMw168qkXJzc0lODiYnJwczGZzUw/HYfq/uNFuYCc2ysyO6UObbFzubOGGA8z6Yi8Ad1xxGUlpuSRnFGACisostA30YfV9g+h/WfBFn7U8MbXFBnobytqlvEYQ1522+IuIiIiIiIiIrbrG1xSM5NINRiqw0zAL1u3nia/22T330v/14ZEhXV08opZFQVwRERERERGR5qeu8bUmbWCzadMmfvnLXxIdHY3JZOLTTz+tdt4wDObOnUtUVBT+/v6MHDmS/fv3V7smKyuLiRMnYjabCQkJYfLkyeTnq74cuH8TGXc1e0R3IoJ8bI6bgCVbT7h+QC3M+JgodswYSuFz49gxY6jWq4iIiIiIiMglpEmDkQUFBfTr149XX33V7vmFCxfyyiuv8Prrr7NlyxYCAwMZPXo0RUVF1msmTpxIUlISa9asYeXKlWzatIn77rvPVVNwewrsNEzOuTKbYwZqpCIiIiIiIiIi0hhus03bZDKxYsUKbrnlFqAiKzI6OpoZM2bw6KOPApCTk0NERASLFy/m9ttvZ+/evfTp04etW7cyYMAAAFatWsWNN97IiRMniI6OtvtaxcXFFBcXW7/Pzc2lffv2l9w2bWk41dsUEREREREREam7ZrFN+0IOHz5MWloaI0eOtB4LDg5m4MCBxMfHAxAfH09ISIg1EAkwcuRIPDw82LJlS63PXrBgAcHBwdav9u3bO28i0izNHdUTg4oAJKgbtoiIiIiIiIiII7htMDItLQ2AiIiIascjIiKs59LS0ggPD6923svLi9DQUOs19syePZucnBzr1/Hjxx08emnuVG9TRERERERERMTxvJp6AE3B19cXX1/fph6GuLnxMVGMV/BRRERERERERMRh3DYzMjIyEoD09PRqx9PT063nIiMjOX36dLXzZWVlZGVlWa8RERERERERERER9+C2wcjOnTsTGRnJunXrrMdyc3PZsmULcXFxAMTFxZGdnc327dut16xfvx6LxcLAgQNdPmYRERERERERERGpXZNu087Pz+fAgQPW7w8fPkxCQgKhoaF06NCBRx55hL/+9a90796dzp0785e//IXo6Ghrx+3evXszZswYpkyZwuuvv05paSnTpk3j9ttvr7WTtoiIiIiIiIiIiDSNJg1Gbtu2jWHDhlm/nz59OgCTJk1i8eLFPPbYYxQUFHDfffeRnZ3Nddddx6pVq/Dz87Pe88EHHzBt2jRGjBiBh4cHEyZM4JVXXnH5XEREREREREREROTCTIZhGE09iKaWm5tLcHAwOTk5mM3mph6OiIiIiIiIiIhIs1LX+Jrb1owUERERERERERGRS4uCkSIiIiIiIiIiIuISCkaKiIiIiIiIiIiISzRpAxt3UVk2Mzc3t4lHIiIiIiIiIiIi0vxUxtUu1p5GwUggLy8PgPbt2zfxSERERERERERERJqvvLw8goODaz2vbtqAxWLh1KlTBAUFYTKZmno4cgnJzc2lffv2HD9+XJ3apUXQmpeWRmteWhqteWlptOalJdK6l4YyDIO8vDyio6Px8Ki9MqQyIwEPDw/atWvX1MOQS5jZbNaHuLQoWvPS0mjNS0ujNS8tjda8tERa99IQF8qIrKQGNiIiIiIiIiIiIuISCkaKiIiIiIiIiIiISygYKeJEvr6+zJs3D19f36YeiohLaM1LS6M1Ly2N1ry0NFrz0hJp3YuzqYGNiIiIiIiIiIiIuIQyI0VERERERERERMQlFIwUERERERERERERl1AwUkRERERERERERFxCwUgRERERERERERFxCQUjRURERERERERExCUUjBS5gE2bNvHLX/6S6OhoTCYTn376abXz6enp3HPPPURHRxMQEMCYMWPYv39/tWtuuOEGTCZTta8HHnig2jXHjh1j3LhxBAQEEB4ezsyZMykrK3P29ETscsW637lzJ3fccQft27fH39+f3r17849//MMV0xOx4arP+kqZmZm0a9cOk8lEdna2k2YlUjtXrvnFixcTGxuLn58f4eHhTJ061ZlTE7HLVWt+69atjBgxgpCQEFq3bs3o0aPZuXOns6cnYsMRax4gPj6e4cOHExgYiNlsZsiQIZw7d856Pisri4kTJ2I2mwkJCWHy5Mnk5+c7e3pyCVAwUuQCCgoK6NevH6+++qrNOcMwuOWWWzh06BCfffYZO3bsoGPHjowcOZKCgoJq106ZMoXU1FTr18KFC63nysvLGTduHCUlJWzevJklS5awePFi5s6d6/T5idjjinW/fft2wsPDef/990lKSuKJJ55g9uzZ/Otf/3L6/ERqcsWar2ry5MnExsY6ZS4ideGqNf/SSy/xxBNPMGvWLJKSkli7di2jR4926txE7HHFms/Pz2fMmDF06NCBLVu28N133xEUFMTo0aMpLS11+hxFqnLEmo+Pj2fMmDGMGjWKH3/8ka1btzJt2jQ8PH4OI02cOJGkpCTWrFnDypUr2bRpE/fdd59L5ijNnCEidQIYK1assH6fnJxsAMbu3butx8rLy42wsDDjjTfesB4bOnSo8fDDD9f63C+//NLw8PAw0tLSrMcWLVpkmM1mo7i42KFzEKkvZ617e/7whz8Yw4YNa+yQRRrF2Wv+tddeM4YOHWqsW7fOAIyzZ886cPQi9eesNZ+VlWX4+/sba9eudcawRRrMWWt+69atBmAcO3bMemzXrl0GYOzfv9+hcxCpj4au+YEDBxpz5syp9bl79uwxAGPr1q3WY1999ZVhMpmMkydPOnYScslRZqRIAxUXFwPg5+dnPebh4YGvry/fffddtWs/+OAD2rZty+WXX87s2bMpLCy0nouPjycmJoaIiAjrsdGjR5Obm0tSUpKTZyFSP45a9/bk5OQQGhrq+EGLNIIj1/yePXuYP38+7777brWsAhF34qg1v2bNGiwWCydPnqR37960a9eOX//61xw/ftw1ExGpI0et+Z49e9KmTRveeustSkpKOHfuHG+99Ra9e/emU6dOLpmLSF3UZc2fPn2aLVu2EB4ezrXXXktERARDhw6t9t9EfHw8ISEhDBgwwHps5MiReHh4sGXLFhfNRpor/UtYpIF69epFhw4dmD17NmfPnqWkpITnn3+eEydOkJqaar3ut7/9Le+//z4bNmxg9uzZvPfee9x5553W82lpadUCkYD1+7S0NNdMRqSOHLXua9q8eTMfffSRtnWI23HUmi8uLuaOO+7ghRdeoEOHDk0xFZE6cdSaP3ToEBaLhWeffZaXX36ZZcuWkZWVxS9+8QtKSkqaYmoidjlqzQcFBbFx40bef/99/P39adWqFatWreKrr77Cy8urKaYmYldd1vyhQ4cAePLJJ5kyZQqrVq3iyiuvZMSIEdbakmlpaYSHh1d7tpeXF6Ghofo5Vi5Kn4oiDeTt7c3y5cuZPHkyoaGheHp6MnLkSMaOHYthGNbrqgZXYmJiiIqKYsSIERw8eJCuXbs2xdBFGswZ63737t3cfPPNzJs3j1GjRrlsLiJ14ag1P3v2bHr37n3BoLyIO3DUmrdYLJSWlvLKK69YP9s//PBDIiMj2bBhg2pHittw1Jo/d+4ckydPZvDgwXz44YeUl5fzt7/9jXHjxrF161b8/f2bYnoiNuqy5i0WCwD3338/9957LwBXXHEF69at4+2332bBggVNNn65NCgzUqQRrrrqKhISEsjOziY1NZVVq1aRmZlJly5dar1n4MCBABw4cACAyMhI0tPTq11T+X1kZKSTRi7ScI5Y95X27NnDiBEjuO+++5gzZ45Txy3SUI5Y8+vXr+fjjz/Gy8sLLy8vRowYAUDbtm2ZN2+e8ychUg+OWPNRUVEA9OnTx3pNWFgYbdu25dixY04cvUj9OWLNL126lCNHjvDOO+9w9dVXM2jQIJYuXcrhw4f57LPPXDIPkbq62Jq39xkO0Lt3b+tneGRkJKdPn652vqysjKysLP0cKxelYKSIAwQHBxMWFsb+/fvZtm0bN998c63XJiQkAD9/wMfFxZGYmFjtg3zNmjWYzWabD38Rd9KYdQ+QlJTEsGHDmDRpEs8884yzhyvSaI1Z85988gk7d+4kISGBhIQE3nzzTQC+/fZbpk6d6vSxizREY9b84MGDAUhOTrZek5WVxZkzZ+jYsaPzBi3SCI1Z84WFhXh4eGAymazXVH5fmWUm4m5qW/OdOnUiOjq62mc4QEpKivUzPC4ujuzsbLZv3249v379eiwWizVYL1IbbdMWuYD8/PxqmVyHDx8mISGB0NBQOnTowMcff0xYWBgdOnQgMTGRhx9+mFtuucW6HengwYMsXbqUG2+8kTZt2rBr1y7+9Kc/MWTIEGJjYwEYNWoUffr04a677mLhwoWkpaUxZ84cpk6diq+vb5PMW1o2V6z73bt3M3z4cEaPHs306dOtdWU8PT0JCwtz/aSlRXPFmq9ZnuDMmTNARYZBSEiIayYqcp4r1nyPHj24+eabefjhh/n3v/+N2Wxm9uzZ9OrVi2HDhjXJvKXlcsWa/8UvfsHMmTOZOnUqDz30EBaLheeeew4vLy+teXG5xq55k8nEzJkzmTdvHv369aN///4sWbKEffv2sWzZMqDi3zBjxoxhypQpvP7665SWljJt2jRuv/12oqOjm2Te0ow0ZStvEXe3YcMGA7D5mjRpkmEYhvGPf/zDaNeuneHt7W106NDBmDNnjlFcXGy9/9ixY8aQIUOM0NBQw9fX1+jWrZsxc+ZMIycnp9rrHDlyxBg7dqzh7+9vtG3b1pgxY4ZRWlrqyqmKWLli3c+bN8/ua3Ts2NHFsxVx3We9vdc8e/ask2cnYstVaz4nJ8f43e9+Z4SEhBihoaHGrbfeahw7dsyVUxUxDMN1a3716tXG4MGDjeDgYKN169bG8OHDjfj4eFdOVcQwjMav+UoLFiww2rVrZwQEBBhxcXHGt99+W+18ZmamcccddxitWrUyzGazce+99xp5eXmumKI0cybDqFKVV0RERERERERERMRJVDNSREREREREREREXELBSBEREREREREREXEJBSNFRERERERERETEJRSMFBEREREREREREZdQMFJERERERERERERcQsFIERERERERERERcQkFI0VERERERERERMQlFIwUERERERERERERl1AwUkRERERERERERFxCwUgRERERERERERFxCQUjRURERERERERExCX+PzlTjSvM39ZpAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_train, y_test = temporal_train_test_split(y)\n",
    "plot_series(y_train, y_test, labels=[\"y_train\", \"y_test\"]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pitfall 2: obscure data manipulations, brittle boilerplate code to apply regressors\n",
    "\n",
    "It is common practice to apply supervised regressors after transforming the data for forecasting, through lagging - for example, in auto-regressive reduction strategies.\n",
    "\n",
    "Two important pitfalls appear right at the start:\n",
    "\n",
    "* a lot of boilerplate code has to be written to transform the data to make it ready for fitting - this is highly error prone\n",
    "* there are a number of implicit hyper-parameters here, such as window and lag size. If done without caution, these are not explicit or tracked in the experiment, which can lead to \"p-value hacking\".\n",
    "\n",
    "Below is an example of such boilerplate code to demonstrate this. The code is closely modelled on the R code used in the [M4 competition](https://github.com/Mcompetitions/M4-methods):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# suppose we want to predict 3 years ahead\n",
    "fh = np.arange(1, 37)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-10T16:07:07.023411Z",
     "iopub.status.busy": "2021-04-10T16:07:07.022907Z",
     "iopub.status.idle": "2021-04-10T16:07:07.024499Z",
     "shell.execute_reply": "2021-04-10T16:07:07.024998Z"
    }
   },
   "outputs": [],
   "source": [
    "# slightly modified code from the M4 competition\n",
    "\n",
    "\n",
    "def split_into_train_test(data, in_num, fh):\n",
    "    \"\"\"Split the series into train and test sets.\n",
    "\n",
    "    Each step takes multiple points as inputs\n",
    "    :param data: an individual TS\n",
    "    :param fh: number of out of sample points\n",
    "    :param in_num: number of input points for the forecast\n",
    "    :return:\n",
    "    \"\"\"\n",
    "    train, test = data[:-fh], data[-(fh + in_num) :]\n",
    "    x_train, y_train = train[:-1], np.roll(train, -in_num)[:-in_num]\n",
    "    x_test, y_test = test[:-1], np.roll(test, -in_num)[:-in_num]\n",
    "    #     x_test, y_test = train[-in_num:], np.roll(test, -in_num)[:-in_num]\n",
    "\n",
    "    # reshape input to be [samples, time steps, features]\n",
    "    # (N-NF samples, 1 time step, 1 feature)\n",
    "    x_train = np.reshape(x_train, (-1, 1))\n",
    "    x_test = np.reshape(x_test, (-1, 1))\n",
    "    temp_test = np.roll(x_test, -1)\n",
    "    temp_train = np.roll(x_train, -1)\n",
    "    for _ in range(1, in_num):\n",
    "        x_train = np.concatenate((x_train[:-1], temp_train[:-1]), 1)\n",
    "        x_test = np.concatenate((x_test[:-1], temp_test[:-1]), 1)\n",
    "        temp_test = np.roll(temp_test, -1)[:-1]\n",
    "        temp_train = np.roll(temp_train, -1)[:-1]\n",
    "\n",
    "    return x_train, y_train, x_test, y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-10T16:07:07.028644Z",
     "iopub.status.busy": "2021-04-10T16:07:07.028108Z",
     "iopub.status.idle": "2021-04-10T16:07:07.029820Z",
     "shell.execute_reply": "2021-04-10T16:07:07.030335Z"
    }
   },
   "outputs": [],
   "source": [
    "# here we split the time index, rather than the actual values,\n",
    "# to show how we split the windows\n",
    "feature_window, target_window, _, _ = split_into_train_test(\n",
    "    np.arange(len(y)), 10, len(fh)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To better understand the prior data transformation, we can look at how we can split the training series into windows. Here we show the generated windows expressed as integer indices:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-10T16:07:07.033532Z",
     "iopub.status.busy": "2021-04-10T16:07:07.033064Z",
     "iopub.status.idle": "2021-04-10T16:07:07.035278Z",
     "shell.execute_reply": "2021-04-10T16:07:07.035836Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9],\n",
       "       [ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10],\n",
       "       [ 2,  3,  4,  5,  6,  7,  8,  9, 10, 11],\n",
       "       [ 3,  4,  5,  6,  7,  8,  9, 10, 11, 12],\n",
       "       [ 4,  5,  6,  7,  8,  9, 10, 11, 12, 13]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_window[:5, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-10T16:07:07.038874Z",
     "iopub.status.busy": "2021-04-10T16:07:07.038346Z",
     "iopub.status.idle": "2021-04-10T16:07:07.040421Z",
     "shell.execute_reply": "2021-04-10T16:07:07.040933Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10, 11, 12, 13, 14])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_window[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-10T16:07:07.044178Z",
     "iopub.status.busy": "2021-04-10T16:07:07.043592Z",
     "iopub.status.idle": "2021-04-10T16:07:07.046140Z",
     "shell.execute_reply": "2021-04-10T16:07:07.046641Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(98, 10) (98,)\n"
     ]
    }
   ],
   "source": [
    "# now we can split the actual values of the time series\n",
    "x_train, y_train, x_test, y_test = split_into_train_test(y.values, 10, len(fh))\n",
    "print(x_train.shape, y_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-10T16:07:07.083439Z",
     "iopub.status.busy": "2021-04-10T16:07:07.082648Z",
     "iopub.status.idle": "2021-04-10T16:07:07.167067Z",
     "shell.execute_reply": "2021-04-10T16:07:07.167558Z"
    }
   },
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "RandomForestRegressor()"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "model = RandomForestRegressor()\n",
    "model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To reiterate the potential pitfalls here:\n",
    "\n",
    "> The manual requires a lot of hand-written code which is often error-prone, not modular and not tuneable.\n",
    "\n",
    "These steps involve a number of implicit hyper-parameters:\n",
    "\n",
    "> * The way you slice the time series into windows (e.g. the window length);\n",
    "> * The way you generate forecasts (recursive strategy, direct strategy, other hybrid strategies)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pitfall 3: Given a fitted regression algorithm, how can we generate forecasts?\n",
    "\n",
    "The next important pitfall comes at the end:\n",
    "\n",
    "If making predictions along the \"manual route\" for supervised regressors, the supervised regressor's outputs have to be transformed back into forecasts. This is easily forgotten, and invites errors in forecasts and evaluation (see pitfall no.1) - especially, if one does not cleanly keep track of which data is known at what time, or how to invert the transformation made in fitting.\n",
    "\n",
    "A naive user might now proceed like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-10T16:07:07.171836Z",
     "iopub.status.busy": "2021-04-10T16:07:07.171233Z",
     "iopub.status.idle": "2021-04-10T16:07:07.173018Z",
     "shell.execute_reply": "2021-04-10T16:07:07.173506Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(36, 10) (36,)\n"
     ]
    }
   ],
   "source": [
    "print(x_test.shape, y_test.shape)\n",
    "\n",
    "# add back time index to y_test\n",
    "y_test = pd.Series(y_test, index=y.index[-len(fh) :])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-10T16:07:07.180067Z",
     "iopub.status.busy": "2021-04-10T16:07:07.177212Z",
     "iopub.status.idle": "2021-04-10T16:07:07.186432Z",
     "shell.execute_reply": "2021-04-10T16:07:07.186959Z"
    }
   },
   "outputs": [],
   "source": [
    "y_pred = model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.10892546588134275"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sktime.performance_metrics.forecasting import mean_absolute_percentage_error\n",
    "\n",
    "mean_absolute_percentage_error(\n",
    "    y_test, pd.Series(y_pred, index=y_test.index), symmetric=False\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So easy, so wrong ... but what's the problem here? It's a bit subtle and not easy to spot:\n",
    "\n",
    "> We actually don't make a multi-step-ahead forecast up to the 36th step ahead. Instead, we make 36 single-step-ahead forecasts always using the most recent data. But that's a solution to a different learning task!\n",
    "\n",
    "To fix this problem, we could write some code to do this recursively as in the M4 competition:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-10T16:07:07.228733Z",
     "iopub.status.busy": "2021-04-10T16:07:07.219457Z",
     "iopub.status.idle": "2021-04-10T16:07:07.421143Z",
     "shell.execute_reply": "2021-04-10T16:07:07.421651Z"
    }
   },
   "outputs": [],
   "source": [
    "# slightly modified code from the M4 study\n",
    "predictions = []\n",
    "last_window = x_train[-1, :].reshape(1, -1)  # make it into 2d array\n",
    "\n",
    "last_prediction = model.predict(last_window)[0]  # take value from array\n",
    "\n",
    "for i in range(len(fh)):\n",
    "    # append prediction\n",
    "    predictions.append(last_prediction)\n",
    "\n",
    "    # update last window using previously predicted value\n",
    "    last_window[0] = np.roll(last_window[0], -1)\n",
    "    last_window[0, (len(last_window[0]) - 1)] = last_prediction\n",
    "\n",
    "    # predict next step ahead\n",
    "    last_prediction = model.predict(last_window)[0]\n",
    "\n",
    "y_pred_rec = pd.Series(predictions, index=y_test.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.19738808156953527"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sktime.performance_metrics.forecasting import mean_absolute_percentage_error\n",
    "\n",
    "mean_absolute_percentage_error(\n",
    "    y_test, pd.Series(y_pred_rec, index=y_test.index), symmetric=False\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To summarize the potential pitfalls here:\n",
    "\n",
    "> Obtaining regressor predictions and converting them back into forecasts is non-trivial and error prone:\n",
    "> * some boilerplate code needs to be written, which just as in pitfall no.2 introduces potential for problems;\n",
    "> * It isn't exactly obvious that this boilerplate code had to be written in the first place, creating a subtle failure point."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How does `sktime` help avoid the above pitfalls?\n",
    "\n",
    "`sktime` mitigates the above pitfalls with:\n",
    "\n",
    "* Its unified interface for forecasters - any strategy to produce forecasts is a forecaster. Through the unified interface, forecasters are directly compatible with deployment and evaluation workflows appropriate for forecasters;\n",
    "* Its declarative specification interface that minimizes boilerplate code - it's minimized to the bare necessities to tell `sktime` which forecaster you want to build.\n",
    "\n",
    "Nevertheless, `sktime` aims to be flexible, and tries to avoid to railroad the user into specific methodological choices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "\n",
    "from sktime.forecasting.compose import make_reduction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# declarative forecaster specification - just two lines!\n",
    "regressor = KNeighborsRegressor(n_neighbors=1)\n",
    "forecaster = make_reduction(regressor, window_length=15, strategy=\"recursive\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "forecaster.fit(y_train)\n",
    "y_pred = forecaster.predict(fh)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "... and that's it!\n",
    "\n",
    "Note that there is no `x_train` or other boilerplate artefacts, since construction of the lagged features and other boilerplate code are taken care of by the forecaster internally.\n",
    "\n",
    "For more details on the `sktime` composition interface, refer to Section 3 of the main forecasting tutorial."
   ]
  }
 ],
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